学科间交叉研究主题识别——以图书情报学与教育学为例  

Research on Interdisciplinary Topics Identification——A Case Study of Library & Information Science and Education

作　　者：阮光册[1] 夏磊[2] RUAN Guang-ce;XIA Lei(Department of Information Management,East China Normal University,Shanghai 200241,China;Exhibition Center of Shanghai Library,Shanghai 20003,China)

机构地区：[1]华东师范大学信息管理系,上海200241 [2]上海图书馆会展中心,上海200031

出　　处：《情报科学》2020年第12期152-157,共6页Information Science

基　　金：上海哲学社会科学一般项目“基于主题模型的学科交叉知识发现研究”(2016BTQ002)。

摘　　要：【目的/意义】科学研究中学科交叉日益广泛。学科间交叉主题的识别,可以发现学科之间的内在关联,是促进学科间合作的基础。【方法/过程】本文将聚类分析、LDA模型、社会关系网络等多种方法相结合,挖掘并揭示学科间交叉研究主题,以及这些主题在不同学科中的关系网络。实验环节,本文选取2017年图书情报学和教育学的56本CSSCI期刊论文进行交叉主题识别及知识网络构建。【结果/结论】通过与关键词共现网络进行对比,本文的方法能够更好的识别学科间交叉研究的特征。【Purpose/significance】In science research, the Interdisciplinary is becoming more and more widespread. The recognition of interdisciplinary topics can reveal the intrinsic relationship between disciplines and is the basis for promoting interdisciplinary cooperation.【Method/process】This paper combines cluster analysis, LDA model, co-occurrence topic knowledge network and other methods to discover and reveal the topics of interdisciplinary and the social networks of these topics in their respective disciplines. In the experiment, this paper selected 56 CSSCI journal papers in 2017 for Library and Information Science and Education for cross-topic identification and knowledge network construction.【Result/conclusion】By comparing with the keyword co-occurrence method, this paper has a better research result.

关 键 词：学科交叉 社会关系网络 聚类分析 主题模型 

分 类 号：G250.2[文化科学—图书馆学]