基于知识图谱的高校教学实验室综合改革模型分析

Analysis of Comprehensive Reform Model of University Teaching Laboratory Based on Knowledge Map

基于BP神经网络的高校教学实验室综合改革模型分析方法,缺乏可视化分析过程,存在分析结果误差大的弊端,为此提出基于知识图谱的高校教学实验室综合改革模型分析方法。该方法以中文论文数据库为样本,采用知识图谱技术和Citespace可视化软件,通过定量化、可视化已有文献的研究热点,绘制高校教学实验室综合改革模型的系列知识图谱,获取高校教学实验室综合改革模型数据。在此基础上采用基于信息归纳的概率粗糙集挖掘算法SRII,将原始数据集映射到一个便于粗糙集处理的数据空间,计算各个属性集合的近似统计值,实现对实验室综合改革模型数据的有效挖掘和分析。实验结果表明,所提方法能有效分析高校教学实验室综合改革模型,呈现出了改革模型未来改革热点的直观分析结果,为深化实验室综合改革提供有力条件。

The comprehensive reform model analysis method of university teaching laboratories based on BP neural network lacks visual analysis process,and has the disadvantage of large error in analysis results.The comprehensive reform model analysis method based on knowledge map is proposed,and the Chinese dissertation database is used as a sample to illustrate the mehtod.Using knowledge map technology and Citespace visualization software,by quantifying and visualizing the research hotspot of existing literature,we draw a series of knowledge maps of the comprehensive reform model of university teaching laboratories,and obtain the comprehensive reform model data of university teaching laboratories.On this basis,SRII,a probabilistic rough set mining algorithm based on information induction,maps the original data set to a data space that is easy for rough set processing,calculates the approximate statistical values of each attribute set,and realizes the effective mining of laboratory comprehensive reform model data.The experimental results show that the proposed method can effectively analyze the comprehensive reform model of teaching laboratories in colleges and universities,present the intuitive analysis results of the reform hot spot of the reform model,and provide powerful conditions for deepening the comprehensive reform of laboratories.