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 scien-tific 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.Exper-iments reveal that the model correctly captures the semantic similarity relationship between scientific and technical litera-ture and their keywords,and that the experimental results properly represent the intersection tendency between the two dis-ciplines.
关键词
科研项目/学科交叉主题/对比学习/表示学习
Key words
research projects/interdisciplinary topic/contrastive learning/representation learning
维普
万方数据