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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0252753 |
| _version_ | 1818736804385259520 |
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| author | June Young Lee Sejung Ahn Dohyun Kim |
| author_facet | June Young Lee Sejung Ahn Dohyun Kim |
| author_sort | June Young Lee |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-18T00:42:58Z |
| format | Article |
| id | doaj.art-692c508692d44781b09be76b1810ee10 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T00:42:58Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-692c508692d44781b09be76b1810ee102022-12-21T21:26:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01166e025275310.1371/journal.pone.0252753Deep learning-based prediction of future growth potential of technologies.June Young LeeSejung AhnDohyun KimResearch 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.https://doi.org/10.1371/journal.pone.0252753 |
| spellingShingle | June Young Lee Sejung Ahn Dohyun Kim Deep learning-based prediction of future growth potential of technologies. PLoS ONE |
| title | 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_short | Deep learning-based prediction of future growth potential of technologies. |
| title_sort | deep learning based prediction of future growth potential of technologies |
| url | https://doi.org/10.1371/journal.pone.0252753 |
| work_keys_str_mv | AT juneyounglee deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies AT sejungahn deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies AT dohyunkim deeplearningbasedpredictionoffuturegrowthpotentialoftechnologies |