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| Multi-Source Data Fusion for Identification of Key Generic Technology: A Knowledge Graph and Deep Learning-Based Approach |
| Zhong Yule1, Yao Zhanlei1,2, Xu Xin1,2 |
1.School of Economics and Management, East China Normal University, Shanghai 200062
2.Shanghai Municipal Experimental Teaching Demonstration Center for Business Analytics, Shanghai 200062 |
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Abstract The precise identification of key generic technology is a pivotal component in the innovation and development of technology and plays a significant role in the strategic layout of industrial technology and investment decision-making. In this study, a novel method was developed for identifying key generic technology by integrating multi-source data. Specifically, a quantifiable key generic technology identification index system was developed based on the fundamental characteristics and features of key generic technology, encompassing five dimensions: universality, associativity, benefit, foundationality, and criticality. By leveraging the semantic representation and retrieval advantages of knowledge graphs, we trained a key generic technology identification model using bidirectional long short-term memory (BiLSTM) attention and employed bidirectional encoder representations from transformers (BERT) topic clustering to formulate a list of candidate key generic technology themes. Subsequently, by incorporating multi-source heterogeneous data, such as news and social media, we introduced technology-society interaction factors to scientifically determine key generic technology. Finally, an empirical study in the field of electrochemical energy storage was conducted to validate the effectiveness of our method. The results demonstrate that our approach provides valuable references for the identification of key generic technology across various domains, scientifically promoting strategic industrial planning and decision-making, which fosters the formation and development of new quality productive forces.
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Received: 12 December 2024
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