Funding map using paragraph embedding based on semantic diversity.

Autor/inn/en	Kawamura, Takahiro; Watanabe, Katsutaro; Matsumoto, Naoya; Egami, Shusaku; Jibu, Mari
Titel	Funding map using paragraph embedding based on semantic diversity.
Quelle	In: Scientometrics, (2018) 2, S.941-958 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-018-2783-x
Schlagwörter	Paragraph embedding; Thesaurus; Information entropy; Map of science
Abstract	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.
Erfasst von	OLC
Update	2023/2/05