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Machine-learning-based deep semantic analysis approach for forecasting new technology convergence

Citations

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Cited by:

  1. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
  2. Yang, Guancan & Liu, Di & Chen, Ling & Lu, Kun, 2025. "Integrating persistence process into the analysis of technology convergence using STERGM," Journal of Informetrics, Elsevier, vol. 19(1).
  3. Chen ZHU & Kazuyuki MOTOHASHI, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
  4. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
  5. Jianyu Zhao & Zhenjie Dong & Xinlin Yao & Xi Xi, 2026. "Optimizing collaboration decisions in technological innovation through machine learning: identify trend and partners in collaboration-knowledge interdependent networks," Annals of Operations Research, Springer, vol. 359(1), pages 1059-1100, April.
  6. Ram, Pappu Kalyan & Pandey, Neeraj & Persis, Jinil, 2024. "Modeling social coupon redemption decisions of consumers in food industry: A machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
  7. Li, Xin & Wang, Yan, 2024. "A novel integrated approach for quantifying the convergence of disruptive technologies from science to technology," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
  8. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
  9. Jeon, Daeseong & Ahn, Joon Mo & Kim, Juram & Lee, Changyong, 2022. "A doc2vec and local outlier factor approach to measuring the novelty of patents," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
  10. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
  11. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
  12. Kangas, H.L. & Ollikka, K. & Ahola, J. & Kim, Y., 2021. "Digitalisation in wind and solar power technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  13. Fu, Mengyu, 2025. "Can low-carbon technology transfer accelerate energy efficiency convergence," Energy, Elsevier, vol. 333(C).
  14. Xi, Fangjie & Wang, Yu & Li, Chenchen & Huang, Ying & Hu, Xiaojun, 2025. "Predicting technological convergence with multi-channel graph neural networks: A case study of CRISPR," Journal of Informetrics, Elsevier, vol. 19(4).
  15. Zhao, Jianyu & Su, Xinjie & Li, Xixi & Xi, Xi & Yao, Xinlin, 2025. "Forecasting technology convergence with the spatiotemporal link prediction model," Technovation, Elsevier, vol. 146(C).
  16. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
  17. Jong Wook Lee & So Young Sohn, 2021. "Patent data based search framework for IT R&D employees for convergence technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5687-5705, July.
  18. Plantec, Quentin & Le Masson, Pascal & Weil, Benoît, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
  19. Singh, Ashutosh & Agarwal, Reeti & Alghafes, Rsha & Papa, Armando, 2025. "Empowering AI with experiential learning: Implications from analysing user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
  20. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
  21. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
  22. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
  23. Bekamiri, Hamid & Hain, Daniel S. & Jurowetzki, Roman, 2024. "PatentSBERTa: A deep NLP based hybrid model for patent distance and classification using augmented SBERT," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
  24. Gui, Liang & Wu, Jie & Liu, Peng & Ma, Tieju, 2025. "Recognition of promising technologies considering inventor and assignee's historic performance: A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 214(C).
  25. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  26. Zhu, Chen & Motohashi, Kazuyuki, 2022. "Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
  27. Büşra Buran & Mehmet Erçek, 2021. "Convergence or Divergence among Business Models of Public Bus Transport Authorities across the Globe: A Fuzzy Approach," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
  28. Hantao Liu & Xiaoyun Zhang & Yang He, 2025. "Digital Transformation and ESG Performance—Empirical Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 17(13), pages 1-23, July.
  29. Wang, Yafei & Shi, Ming & Liu, Junnan & Zhong, Min & Ran, Rong, 2025. "The impact of digital-real integration on energy productivity under a multi-governance framework: The mediating role of AI and embodied technological progress," Energy Economics, Elsevier, vol. 142(C).
  30. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
  31. Yang, Guancan & Xing, Jiaxin & Xu, Shuo & Zhao, Yuntian, 2024. "A framework armed with node dynamics for predicting technology convergence," Journal of Informetrics, Elsevier, vol. 18(4).
  32. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
  33. Chen Zhu & Kazuyuki Motohashi, 2023. "Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3035-3065, May.
  34. Huang, Yiwei & Xu, Shuqi & Cai, Shimin & Lü, Linyuan, 2026. "Uncovering multi-technology convergence patterns with hypergraphs: Evolution and prediction using patent data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 682(C).
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