| 基于全域微观模型的研究前沿主题探测和特征分析 |
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| 引用本文： | 崔宇红,王飒,高晓巍,杨卉,曹学伟.基于全域微观模型的研究前沿主题探测和特征分析[J].图书情报工作,2018,62(15):75-82. |
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| 作者姓名： | 崔宇红 王飒 高晓巍 杨卉 曹学伟 |
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| 作者单位： | 1. 北京理工大学图书馆 北京 100081;
2. 中国科协创新战略研究院 北京 100012;
3. 励德爱思唯尔信息技术(北京)有限公司上海分公司 上海 200040 |
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| 基金项目： | 本文系中国科协创新战略研究院研究课题"基于大数据和科学计量方法的科技前沿探测研究"成果之一。 |
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| 摘 要： | 目的/意义]研究前沿的准确判断是国家宏观层面的战略需求，文献计量学作为一种定量研究方法广泛应用于科学主题探测和研究前沿识别中。方法/过程]梳理研究前沿主题探测的发展历程和方法模型，引入全域微观模型的概念，详细介绍SciVal模块采用的主题创建方法，包括直接引用文献聚类、关键词主题命名和研究前沿遴选的主题显著性算法，并对SciVal创建的9.6万个主题和遴选出的前1%的研究前沿主题的特征进行实证分析。结果/结论]全域微观模型可以同时一次识别整个科学领域的所有主题，但不同学科在研究前沿上表现存在差异，不能把主题显著性简单等同为重要性；主题论文数量与主题排名之间存在中度相关性；自动抽取的关键词术语从学科领域层和独特性上命名和描述主题；石墨烯相关前沿主题的演变趋势分析可以用于发现关键节点和新兴主题。
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| 关 键 词： | 主题探测 研究前沿 全域微观模型 SciVal 主题显著性 |
| 收稿时间： | 2017-12-08 |
Detecting and Characterizing Research Fronts Topics Based on Global-Micro Model |
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| Cui Yuhong,Wang Sa,Gao Xiaowei,Yang Hui,Cao Xuewei.Detecting and Characterizing Research Fronts Topics Based on Global-Micro Model[J].Library and Information Service,2018,62(15):75-82. |
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| Authors: | Cui Yuhong Wang Sa Gao Xiaowei Yang Hui Cao Xuewei |
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| Institution: | 1. Beijing Institute of Technology Library, Beijing 100081;
2. National Academy of Innovation Strategy, Beijing 100012;
3. Relx Group Shanghai District, Shanghai 200040 |
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| Abstract: | Purpose/significance] Accurate judgment of research fronts is the national strategic macro-level demand, and scientometrics is commonly used in the quantitative method of research fronts and topic detection. Method/process] Firstly,literature review is focused on topic detection and research fronts,then concept of the global-micro model and methods in topic creation are introduced in detail, including topic cluster with direct citation,name label with keyword, and selection methodology of topic prominence. It also analyzes nearly 96,000 topics and the top 1% research fronts created by Scival. Result/conclusion] The global-micro model can identify all topics of different fields at the same time, but there are differences in the research fronts between different subjects, which can not equate topic prominence to the importance of simplicity. There is a moderate correlation between the number of topic papers and the topic ranking. Automatically extracted keywords can be named and described the topic in terms of the subject level and uniqueness. The topic evolution is demonstrated by the related research fronts of graphene, which can be used to identify key events and emerging trends. |
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| Keywords: | topic detection research fronts global-micro model SciVal topic prominence |
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