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ITGInsight–discovering and visualizing research fronts in the scientific literature

Author

Listed:
  • Xuefeng Wang

    (Beijing Institute of Technology)

  • Shuo Zhang

    (Beijing Institute of Technology)

  • Yuqin liu

    (Beijing Institute of Graphic Communication)

Abstract

Nowadays, most organizations face the challenge of having to track the latest technological developments so as to discover new technology opportunities and to identify threats in their competitive environment. The capacity to do this relies heavily on the ability to recognize scientific innovation. Hence, monitoring emerging research directions in the scientific literature has become an important task for both researchers and policy makers. Yet the best method of doing so is still a topic of controversy. Our goal is to develop a generic computational framework that can describe a research domain in terms of its research fronts and further track the evolution trends of the knowledge structures behind each research front for the purposes of identifying knowledge innovation. The results show the evolution trends of knowledge structures could lead up to pioneering research. Implemented in ITGInsight, a C# application, the modelling and visualization process incorporates a topic clustering model and a topic evolution model to reveal knowledge structures and their evolution trends. Using the framework in a case study on synthetic biology, we verified the results it produced by consulting the literature and a panel of domain experts. The tool proves to be powerful font of insightful information that would be difficult and time-consuming for researchers and policy makers to gather on their own. Anyone involved in R&D planning, research funds allocation, and technology opportunity analysis will find the framework useful.

Suggested Citation

  • Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:11:d:10.1007_s11192-021-04190-9
    DOI: 10.1007/s11192-021-04190-9
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