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| Research-Front Identification Based on Multilayer Semantic Networks |
| Liang Guoqiang, Qiu Xiaopeng, Huang Xu, Zhang Shuo, Zhang Zhihao, Lin Gege |
| College of Economics and Management, Beijing University of Technology, Beijing 100124 |
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Abstract To address the semantic deficiency issues present in conventional research-front identification methods, i.e., citation analysis, text mining, and machine learning, this study proposes research-front identification based on multilayer semantic networks. DeepSeek-V3 was employed to extract subject-predicate-object (SPO) triplets in abstracts to facilitate the construction of multilayer semantic networks. Subsequently, cross-layer neighbor entropy and burst strength indicators were utilized to detect the activity and emergence characteristics of research fronts, with reverse matching of key nodes performed to obtain semantic information. Finally, empirical analysis was conducted with reference to carbon-capture literature published between 2014 and 2024 to evaluate the effectiveness of this method. The results show that the proposed method successfully identified approximately 80 emerging nodes and their semantic relationships in the carbon-capture field. Since 2024, these semantic relationships have focused on topics including “soil carbon sequestration,” “climate change,” “straw return,” “forest carbon sequestration,” and “model,” thus indicating that these topics represent research frontiers in the carbon-capture field. The extraction of SPO triplets based on large language models improves the efficiency and quality of the entities extracted. The proposed method addresses the semantic deficiency issues inherent in conventional research-front identification approaches and identifies research fronts in a research field.
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Received: 01 September 2025
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