AI Article Synopsis
- The study combines keyword analysis and social network analysis to visualize and quantify knowledge structures in scientific research.
- This involves creating both three-dimensional networks (like "Research focus parallelship" and "Keyword Co-occurrence Networks") and a two-dimensional knowledge map to represent information at different levels (macro, meso, micro).
- The analysis of 223 highly cited papers reveals that China and the US are central to this knowledge structure, with China leading, offering insights into emerging trends in scientific development and enabling better management of scientific research.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126463 | PMC |
| http://dx.doi.org/10.1016/j.techfore.2010.06.002 | DOI Listing |
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