Comparative evaluation of bibliometric content networks by tomographic content analysis: An application to Parkinson's disease
研究了三种文献计量内容网络（基于主题关键词、下层概念和主题影响）的特性，发现它们分别适用于理解当前知识、跨领域发现新知识和勾勒主题全貌，对帕金森病研究有参考价值。
To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice‐and‐dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.