Funding map using paragraph embedding based on semantic diversity

Scientometrics
Takahiro KawamuraMari Jibu

Abstract

Maps of science representing the structure of science can help us understand science and technology (S&T) development. Studies have thus developed techniques for analyzing research activities' relationships; however, ongoing research projects and recently published papers have difficulty in applying inter-citation and co-citation analysis. Therefore, in order to characterize what is currently being attempted in the scientific landscape, this paper proposes a new content-based method of locating research projects in a multi-dimensional space using the recent word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with the original paragraph vectors, we introduce paragraph vectors based on the information entropies of concepts in an S&T thesaurus. The experimental results show that the proposed method successfully formed a clustered map from 25,607 project descriptions of the 7th Framework Programme of EU from 2006 to 2016 and 34,192 project descriptions of the National Science Foundation from 2012 to 2016.

References

Jul 30, 1965·Science·D J PRICE
Feb 12, 2004·Proceedings of the National Academy of Sciences of the United States of America·Thomas L Griffiths, Mark Steyvers
May 31, 2011·Nature Methods·Edmund M TalleyAndrew McCallum

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Software Mentioned

GLOBAL
Deep Learning Library for Java
Word2vec
STAR METRIC
pLSA
Sci2 Team
OpenOrd

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