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Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

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  • Qian, Yue
  • Liu, Yu
  • Sheng, Quan Z.

Abstract

Detecting what type of knowledge constitutes a discipline, tracking how the knowledge changes, and understanding why the changes are triggered are the key issues in analyzing scientific development from a macro perspective, which is usually analyzed by the topic of evolution. However, traditional methods assume that the disciplinary structure is flat with only one-layer topics, rather than a tree-like structure with hierarchical topics, which leads to the inability of existing methods to effectively examine the details of the evolution, such as the interactions between different research directions. In this paper, we take artificial intelligence (AI) as a case in which we study its hierarchical structural evolution, more precisely inspecting disciplinary development, by analyzing 65,887 AI-related research papers published during a 10-year period from 2009 to 2018. From a hierarchical topic model that can construct a topic-tree with multi-layer organizations, we design a visual analysis model for the topic-tree to systematically and visually investigate how knowledge transfers along the topic-tree and how the topic-tree changes over time. Moreover, some assistant indicators are employed to help in the exploration of the complicated structural evolution. Then, we discover the latent relationship between the sub-structures within a topic as well as the triggering reason for the knowledge migration. Based on these results, we conclude that different topics have different development patterns and that the recent artificial intelligence revolution stems from the interactions among the different topics.

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  • Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:3:s1751157719302925
    DOI: 10.1016/j.joi.2020.101047
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    2. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    3. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    4. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    5. Zhang, Tongyang & Sun, Ran & Fensel, Julia & Yu, Andrew & Bu, Yi & Xu, Jian, 2023. "Understanding the domain development through a word status observation model," Journal of Informetrics, Elsevier, vol. 17(2).
    6. Sharma, Anuj & Nunkoo, Robin & Rana, Nripendra P. & Dwivedi, Yogesh K., 2021. "On the intellectual structure and influence of tourism social science research," Annals of Tourism Research, Elsevier, vol. 91(C).
    7. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    8. Seyyed Reza Taher Harikandeh & Sadegh Aliakbary & Soroush Taheri, 2023. "An embedding approach for analyzing the evolution of research topics with a case study on computer science subdomains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1567-1582, March.
    9. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).

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