Deep learning-based prediction of future growth potential of technologies.

Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of kno...

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Autores principales:	June Young Lee, Sejung Ahn, Dohyun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/692c508692d44781b09be76b1810ee10
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spelling	oai:doaj.org-article:692c508692d44781b09be76b1810ee102021-12-02T20:07:17ZDeep learning-based prediction of future growth potential of technologies.1932-620310.1371/journal.pone.0252753https://doaj.org/article/692c508692d44781b09be76b1810ee102021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252753https://doaj.org/toc/1932-6203Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased. Therefore, this study devised a model based on meta-knowledge (i.e., text information including citations, abstracts, area codes) for prediction of future growth potential using deep learning algorithms and investigated the applicability of the various forms of meta-knowledge to the prediction of future growth potential. It also proposes how to select the promising technology clusters based on the proposed model.June Young LeeSejung AhnDohyun KimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252753 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q June Young Lee Sejung Ahn Dohyun Kim Deep learning-based prediction of future growth potential of technologies.
description	Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased. Therefore, this study devised a model based on meta-knowledge (i.e., text information including citations, abstracts, area codes) for prediction of future growth potential using deep learning algorithms and investigated the applicability of the various forms of meta-knowledge to the prediction of future growth potential. It also proposes how to select the promising technology clusters based on the proposed model.
format	article
author	June Young Lee Sejung Ahn Dohyun Kim
author_facet	June Young Lee Sejung Ahn Dohyun Kim
author_sort	June Young Lee
title	Deep learning-based prediction of future growth potential of technologies.
title_short	Deep learning-based prediction of future growth potential of technologies.
title_full	Deep learning-based prediction of future growth potential of technologies.
title_fullStr	Deep learning-based prediction of future growth potential of technologies.
title_full_unstemmed	Deep learning-based prediction of future growth potential of technologies.
title_sort	deep learning-based prediction of future growth potential of technologies.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/692c508692d44781b09be76b1810ee10
work_keys_str_mv	AT juneyounglee deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies AT sejungahn deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies AT dohyunkim deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies
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Deep learning-based prediction of future growth potential of technologies.

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