Abstract
Maps of science representing the structure of science can help us understand science and technology (ST however, navigating the recent scientific landscape is still challenging, since conventional inter-citation and co-citation analysis has difficulty in applying to ongoing projects and recently published papers. Therefore, in order to characterize what is being attempted in the current scientific landscape, this paper proposes a content-based method of locating research projects in a multi-dimensional space using word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with paragraph vectors, we introduce cluster vectors based on the information entropies of concepts in an S&T thesaurus. In addition, we propose three approaches to find the semantics of project relationships. The experimental results show that the proposed method successfully formed a clustered graph from 25,607 project descriptions from the 7th Framework Programme of EU from 2006 to 2016. Finally, we evaluated the distances and semantics of the project relationships and identified significant relationships from the graph.
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