Identification and Feature Computation of Interdisciplinary Scientific Terms in the Perspective of Transdis-ciplinary Knowledge Diffusion
[Purpose/Significance]The scientific terms carry the basic knowledge and core concepts of disci-plines.The knowledge mining and feature computation of scientific terms in transdisciplinary academic full-text cita-tions are of great significance for in-depth investigation of the cross-fertilization pattern of transdisciplinary knowledge systems and transdisciplinary influence.[Method/Process]This paper took information science as an example.In the case of unlabeled transdisciplinary corpus,based on the acquired authoritative scientific terms knowledge system,it obtained the learning corpus of transdisciplinary knowledge diffusion of scientific terms extraction and classification with the help of word sequence annotation model and remote supervision.Then,it explored the optimal model based on deep learning,from the perspective of knowledge discovery,defined the new scientific terms discriminative rules,and finally it carried out the multidimensional feature computation such as discipline distribution,citation section dis-tribution,concept specificity,etc.,of the transdisciplinary knowledge diffusion of scientific terms.[Result/Conclusion]The overall performance of RoBERTa-based model is optimal in various indexes,with an F-score of 98.08％,which indicates that the algorithm can ensure the reliability and effectiveness of the identification of transdisciplinary knowl-edge diffusion scientific terms.The scientific terms recognition method based on remote supervision and deep learning is conducive to mining the knowledge of transdisciplinary knowledge diffusion scientific terms,which can provide domain-oriented basic computing resources support for intelligent knowledge mining of transdisciplinary knowledge diffusion.The multidimensional feature computation can effectively explore the cross-fertilization pattern of transdis-ciplinary knowledge diffusion scientific terms granularity.
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