Lee Pei-Chun, Su Hsin-Ning
SPRU, Science and Technology Policy Research, The Freeman Centre, University of Sussex, Brighton, BN1 9QE, UK.
Graduate Institute of Technology and Innovation Management, National Chengchi University, No. 64, Sec. 2, Chih-nan Rd. Wenshan, Taipei, 116, Taiwan.
Technol Forecast Soc Change. 2011 Jan;78(1):132-151. doi: 10.1016/j.techfore.2010.06.002. Epub 2010 Jul 10.
This study aims to understand knowledge structure both quantitatively and visually by integrating keyword analysis and social network analysis of scientific papers. The methodology proposed in this study is capable of creating a three-dimensional "Research focus parallelship network" and a "Keyword Co-occurrence Network", together with a two-dimensional knowledge map. The network and knowledge map can be depicted differently by choosing different information for the network actor, i.e. country, institute, paper and keyword, to reflect knowledge structures from macro, to meso, to micro-levels. A total of 223 highly cited papers published by 142 institutes and 26 countries are analyzed in this study. China and the US are the two countries located at the core of knowledge structure and China is ranked no. 1. This quantitative exploration provides a way to unveil important or emerging components in scientific development and also to visualize knowledge; thus an objective evaluation of scientific research is possible for quantitative technology management.
本研究旨在通过整合科学论文的关键词分析和社会网络分析，从定量和可视化两个方面理解知识结构。本研究提出的方法能够创建一个三维的“研究焦点平行网络”和一个“关键词共现网络”，以及一个二维知识图谱。通过为网络主体选择不同的信息，即国家、机构、论文和关键词，可以以不同方式描绘网络和知识图谱，以反映从宏观到中观再到微观层面的知识结构。本研究共分析了142个机构和26个国家发表的223篇高被引论文。中国和美国是位于知识结构核心的两个国家，中国排名第一。这种定量探索提供了一种揭示科学发展中重要或新兴组成部分的方法，也能使知识可视化；因此，对于定量技术管理而言，可以对科学研究进行客观评估。
