知识主题演化预测研究

Research on the Evolution and Prediction of Knowledge Topic

【目的/意义】针对现有主题演化方法难以满足预测目的的需求,本文从知识动态发展的角度出发,构建知识主题演化预测模型,为探究科学领域发展脉络与研究趋势提供方法。【方法/过程】通过Lda模型抽取知识主题,利用马尔可夫和隐马尔可夫构建主题稳态与主题热度的演化预测模型。【结果/结论】以云计算领域的科学文献作为实证分析对象,结果表明本模型可以根据历史数据来预测知识主题稳态分布情况与未来热度趋势,且在热度预测精度上较灰色模型更高。【创新/局限】本文只考虑了横向主题内部的热度高低变化,没有进行纵向维度上各知识主题间的对比。

【Purpose/significance】In view of the existing topic evolution method is difficult to meet the demand of prediction purpose.This article to build knowledge topic evolution prediction model from the perspective of knowledge dynamic development,which provide a new way to explore the development and trends of science filed.【Method/process】We apply the LDA model to extract knowledge topics, and build topic steady-state and topic heat evolution prediction model by using Markov model and hidden Markov model.【Result/conclusion】With scientific literature in the field of cloud computing as the object of empirical analysis, the results show that our model can forecast the knowledge topic steady-state distribution and heat trend based on historical data, and the heat prediction accuracy is higher than that of grey model.【Innovation/limitation】This paper only considers the heat change within the topic in the horizontal dimension, and does not compare the knowledge topics in the vertical dimension.