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A systematic review on literature-based discovery workflow
As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924697/ https://www.ncbi.nlm.nih.gov/pubmed/33816888 http://dx.doi.org/10.7717/peerj-cs.235 |
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author | Thilakaratne, Menasha Falkner, Katrina Atapattu, Thushari |
author_facet | Thilakaratne, Menasha Falkner, Katrina Atapattu, Thushari |
author_sort | Thilakaratne, Menasha |
collection | PubMed |
description | As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to the input component, we discuss the data types and data sources used in the literature. The process component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the output component focuses on the visualisation techniques used in LBD discipline. As for the evaluation component, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions. |
format | Online Article Text |
id | pubmed-7924697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79246972021-04-02 A systematic review on literature-based discovery workflow Thilakaratne, Menasha Falkner, Katrina Atapattu, Thushari PeerJ Comput Sci Data Mining and Machine Learning As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to the input component, we discuss the data types and data sources used in the literature. The process component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the output component focuses on the visualisation techniques used in LBD discipline. As for the evaluation component, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions. PeerJ Inc. 2019-11-18 /pmc/articles/PMC7924697/ /pubmed/33816888 http://dx.doi.org/10.7717/peerj-cs.235 Text en ©2019 Thilakaratne et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Mining and Machine Learning Thilakaratne, Menasha Falkner, Katrina Atapattu, Thushari A systematic review on literature-based discovery workflow |
title | A systematic review on literature-based discovery workflow |
title_full | A systematic review on literature-based discovery workflow |
title_fullStr | A systematic review on literature-based discovery workflow |
title_full_unstemmed | A systematic review on literature-based discovery workflow |
title_short | A systematic review on literature-based discovery workflow |
title_sort | systematic review on literature-based discovery workflow |
topic | Data Mining and Machine Learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924697/ https://www.ncbi.nlm.nih.gov/pubmed/33816888 http://dx.doi.org/10.7717/peerj-cs.235 |
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