Analysis Framework for the Evolution of Scientific Themes from a Multi-Dimensional Perspective
Wang Kang1, Chen Yue1, Su Cheng2, Zhao Xiaoyuan2
1.Institution of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116024 2.Institute of Scientific and Technical Information of China, Beijing 100038
Abstract This article is based on knowledge measurement theory regarding the dissociation and combination of knowledge units. Keywords were extracted after time-weighted correction and used as knowledge units. Analysis was based on the theory of scientific theme evolution, occurring at three levels—keyword, keyword association, and topic association framework—and involved measures of time-weighted word frequency, keyword-related topics, and topic-related similarity. The subject of this case study was the core paper for big data research in the field of library and information sciences. Experimental results show that time-weighted keyword frequency measurement strengthens rising keywords, weakens falling keywords, and quickly detects absolute high-frequency words, emergent words, or emerging words. Topic measurement based on keyword associations allows researchers to have an overall perception of and to predict development trends in big data topics in the field of library and information sciences. The similarity measure, which is based on topic association, demonstrates the complex relationships of fusion, diffusion, emergence, and extinction between various topics, which helps reveal research hotspots in the field and predicts future development trends.
Wang Kang,Chen Yue,Su Cheng, et al. Analysis Framework for the Evolution of Scientific Themes from a Multi-Dimensional Perspective[J]. 情报学报, 2021, 40(3): 297-307.
