Integrative model for discovering linked topics in science and technology
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DOI: 10.1016/j.joi.2022.101265
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Cited by:
- Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
- Ba, Zhichao & Meng, Kai & Ma, Yaxue & Xia, Yikun, 2024. "Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
- Arida Ferti Syafiandini & Jeeyoung Yoon & Soobin Lee & Chaemin Song & Erjia Yan & Min Song, 2024. "Examining between-sectors knowledge transfer in the pharmacology field," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3115-3147, June.
- Chen, Xi & Mao, Jin & Li, Gang, 2024. "A co-citation approach to the analysis on the interaction between scientific and technological knowledge," Journal of Informetrics, Elsevier, vol. 18(3).
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Keywords
Linked topics in science and technology; Scientific innovation; Science and technology linkage; Link prediction; Topic recognition;All these keywords.
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