基于关联规则挖掘的多学科知识融合研究——以新冠肺炎研究领域为例  

Study on Multidisciplinary Knowledge Fusion Based on Association Rule Mining——Taking the Research Field of COVID-19 as an Example

作　　者：温芳芳[1] 郑诗嘉 Wen Fangfang;Zheng Shijia(School of Management,Henan University of Science and Technology,Luoyang 471023,China)

机构地区：[1]河南科技大学管理学院,河南洛阳471023

出　　处：《现代情报》2023年第3期148-156,共9页Journal of Modern Information

基　　金：国家社会科学基金一般项目“自引视角下学者研究兴趣的演化路径与迁移规律研究”(项目编号:20BTQ089)。

摘　　要：[目的/意义]挖掘高强度关联学科,揭示多学科知识融合规律,有助于更好地把握和推动多学科知识融合。[方法/过程]从Web of Science核心集获取新冠肺炎主题论文,采用Apriori算法挖掘参考文献所属学科的频繁项集和强关联规则,揭示知识融合特征。以一项强关联规则为例,结合关键词聚类分析,识别该学科组合在知识融合后形成的热门主题。[结果/结论]新冠肺炎研究的知识来源非常广泛,跨学科知识融合十分普遍,关联规则挖掘提供了一种识别和预测强关联学科组合及其知识融合趋势的新方案。[Purpose/Significance]Mining high-intensity related disciplines and revealing the law of multidisciplinary knowledge fusion will help to better grasp and promote multidisciplinary knowledge fusion.[Methods/Process]Based on COVID-19 papers obtained from Web of Science,this study used the Apriori algorithm to mine the association rules of the reference subject category transaction set,and obtained frequent itemsets and strong association rules to analyze and predict the combination of strongly related disciplines and their knowledge fusion characteristics in the field of COVID-19.Taking one of the strong association rules as an example,combined with keyword clustering analysis,this paper further identified the hot topics formed by the multidisciplinary combination after knowledge fusion.[Results/Conclusions]The knowledge sources of COVID-19 research are very extensive,and interdisciplinary knowledge fusion is very common.Association rule mining applied to citation analysis provides a new solution for predicting and identifying strongly related subject combinations and their knowledge fusion trends.

关 键 词：知识融合 学科交叉 关联规则挖掘 APRIORI算法 

分 类 号：G203[文化科学—传播学]