LAUSR

Maps of science representing the structure of science help us understand science and technology development. Thus, research in scientometrics has developed techniques for analyzing research activities and for measuring their relationships; however, navigating the recent scientific landscape is still challenging, since conventional inter-citation and co-citation analysis has difficulty in applying to recently published articles and ongoing projects. Therefore, to characterize what is being attempted in the current scientific landscape, this article proposes a content-based method of locating research articles/ projects in a multi-dimensional space using word/paragraph embedding. Specifically, for addressing an unclustered problem, we introduced cluster vectors based on the information entropies of technical concepts. The experimental results showed that our method formed a clustered map from approx. 300 k IEEE articles and NSF projects from 2012 to 2016. Finally, we confirmed that formation of specific research areas can be captured as changes in the network structure.