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Frontier knowledge discovery and visualization in cancer field based on KOS and LDA

Author

Listed:
  • Qingqiang Wu

    (Xiamen University)

  • Yichen Kuang

    (Xiamen University)

  • Qingqi Hong

    (Xiamen University)

  • Yingying She

    (Xiamen University)

Abstract

Scientific research journals have achieved the latest development in scientific research in various fields. However, the interpretation and use of biomedical information is still a very complicated issue. How to use practical methods to interpret biomedical literature into structured data and analyze it into what we can understand has become a major issue. In this paper, a frontier knowledge discovery model based on KOS and LDA is proposed and applied in detecting burst topic and its sematic information relationship in cancer field. Experiments showed that the model plays an important role in topic recognition, evolution recognition and visualization. Furthermore, the application of KOS combined with LDA can effectively remove noisy concept in sematic layer and show a good effect.

Suggested Citation

  • Qingqiang Wu & Yichen Kuang & Qingqi Hong & Yingying She, 2019. "Frontier knowledge discovery and visualization in cancer field based on KOS and LDA," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 979-1010, March.
    • Handle: RePEc:spr:scient:v:118:y:2019:i:3:d:10.1007_s11192-018-2989-y
    DOI: 10.1007/s11192-018-2989-y
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    References listed on IDEAS

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    1. Marcia Lei Zeng & Lois Mai Chan, 2004. "Trends and issues in establishing interoperability among knowledge organization systems," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 377-395, March.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    3. Zhengyin Hu & Shu Fang & Tian Liang, 2014. "Empirical study of constructing a knowledge organization system of patent documents using topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 787-799, September.
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    Cited by:

    1. Hong, Ming & Wang, Heyong, 2021. "Research on customer opinion summarization using topic mining and deep neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 88-114.

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