Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] As an exploratory research, this paper is oriented to the needs of scientific and technological information in the specialized discipline domain, and aims to realize the quantitative analysis of key semantics of the full text and the practical application shift from "information automation" to "knowledge automation". On the basis of previous studies from the perspective of knowledge element co-occurrence to explore the evolution mechanism of ESI research fronts, this paper further proposes a research front knowledge evolution analysis method based on knowledge element variation. [Method/process] Firstly, knowledge elements were represented as word vectors by word2vec word embedding model. Then, this paper calculated Euclidean distance of knowledge element vectors, and identified knowledge element clusters with similar semantic and pragmatic association by K-means clustering method. Finally, TF-IDF values of each knowledge element in the diachronic cluster were calculated. Through the quantitatively measurement of sudden changes in the importance of knowledge elements, the characteristics and rules of knowledge element variation were mined in the process of ESI research fronts evolution. [Result/conclusion] Through the comparative test of the detection results, it is found that the scientometric method based on knowledge element variation is not only a supplement and expansion of the previous research methods, but also makes the mining of the internal knowledge movement law of ESI research fronts more specific and detailed. Moreover, in the scope of time series, it is a strong evidence that the future development trend and key information signals of the ESI research fronts can be detected as soon as possible.
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