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| Interdisciplinary Topic Identification Method Based on Semantic Similarity Relationship |
| Wang Weijun1,2,3, Ning Zhiyuan2,3, Dong Hao2,3, Qiao Ziyue2,3, Du Yi2,3, Zhou Yuanchun2,3 |
1.Library of Henan University of Economics and Law, Zhengzhou 450046
2.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190
3.University of Chinese Academy of Sciences, Beijing 100049 |
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Abstract Identifying the research content shared among different disciplines is the research idea of interdisciplinary knowledge discovery. Research content with similar semantics better reflects the integration and exchange of knowledge between disciplines. To address the problem of obtaining semantically similar interdisciplinary research topics from scientific and technical literature data, this study proposes an unsupervised contrastive learning method for semantic similarity relationship representation learning of scientific and technical literature and keywords, and then constructs a semantically similar interdisciplinary topic identification model. The model uses the Spearman correlation coefficient as an index for evaluating interdisciplinary topics, thus addressing the lack of interdisciplinary research datasets in current research. Experiments reveal that the model correctly captures the semantic similarity relationship between scientific and technical literature and their keywords, and that the experimental results properly represent the intersection tendency between the two disciplines.
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Received: 15 February 2023
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