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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...

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Detalles Bibliográficos
Autores principales: Thilakaratne, Menasha, Falkner, Katrina, Atapattu, Thushari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
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.
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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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