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| Research on Scientific Topic Prediction from the Perspective of Knowledge Unit Reorganization |
| Liang Jiwen1,2, Yang Jianlin1,2, Wang Wei1,2 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Jiangsu Key Laboratory of Data Engineering & Knowledge Service, Nanjing 210023 |
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Abstract Accurate scientific topic prediction can clarify the future development direction of a given discipline and provide a reference for the development planning and management decision-making in the field of scientific research. This paper focuses on the prediction of new scientific topics based on the perspective of knowledge unit reorganization, compares the representation relationship between the topic and feature words to the representation relationship between scientific concepts and knowledge units, and proposes a scientific topic prediction method. First, the LDA (latent Dirichlet allocation) topic model is used to obtain the global topic, feature words, and probability matrix and obtains the feature word vector by transposing the vector space; second, the vector adjustment coefficients are calculated based on the feature word frequencies predicted by the ARIMA (autoregressive integrated moving average model) model to obtain the feature word prediction vectors, the t-SNE (t-distributed stochastic neighbor embedding) algorithm is applied to reduce the dimensionality of the prediction vectors, and then the low-dimensional prediction vectors are clustered by the fuzzy C-mean algorithm to generate prediction topics to realize the reorganization of knowledge units. Finally, the prediction topic with a new interpretation is selected from the aggregation of several original topics, and this is regarded as the scientific topic prediction result. This paper takes the field of “knowledge management-knowledge organization-knowledge service” as an example for conducting empirical research. The results show that the proposed scientific topic prediction method in this paper can effectively predict new scientific topics from which the essential concepts and the corresponding research content of some words have not appeared at that time, such as digital humanities and knowledge payment.
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Received: 11 May 2022
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