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Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature
The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163941/ https://www.ncbi.nlm.nih.gov/pubmed/37156393 http://dx.doi.org/10.1016/j.jbi.2023.104382 |
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author | Badenes-Olmedo, Carlos Corcho, Oscar |
author_facet | Badenes-Olmedo, Carlos Corcho, Oscar |
author_sort | Badenes-Olmedo, Carlos |
collection | PubMed |
description | The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The work contains best practices for acquiring scientific publications, tuning language models to identify and normalize relevant entities, creating representational models based on probabilistic topics, and formalizing an ontology that describes the associations between domain concepts supported by the scientific literature. All the resources generated in the domain of coronavirus are available openly as part of the Drugs4COVID initiative, and can be (re)-used independently or as a whole. They can be exploited by scientific communities conducting research related to SARS-CoV-2/COVID-19 and also by therapeutic communities, laboratories, etc., wishing to find and understand relationships between symptoms, drugs, active ingredients and their documentary evidence. |
format | Online Article Text |
id | pubmed-10163941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Author(s). Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101639412023-05-08 Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature Badenes-Olmedo, Carlos Corcho, Oscar J Biomed Inform Original Research The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The work contains best practices for acquiring scientific publications, tuning language models to identify and normalize relevant entities, creating representational models based on probabilistic topics, and formalizing an ontology that describes the associations between domain concepts supported by the scientific literature. All the resources generated in the domain of coronavirus are available openly as part of the Drugs4COVID initiative, and can be (re)-used independently or as a whole. They can be exploited by scientific communities conducting research related to SARS-CoV-2/COVID-19 and also by therapeutic communities, laboratories, etc., wishing to find and understand relationships between symptoms, drugs, active ingredients and their documentary evidence. The Author(s). Published by Elsevier Inc. 2023-06 2023-05-06 /pmc/articles/PMC10163941/ /pubmed/37156393 http://dx.doi.org/10.1016/j.jbi.2023.104382 Text en © 2023 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Badenes-Olmedo, Carlos Corcho, Oscar Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title | Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title_full | Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title_fullStr | Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title_full_unstemmed | Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title_short | Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature |
title_sort | lessons learned to enable question answering on knowledge graphs extracted from scientific publications: a case study on the coronavirus literature |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163941/ https://www.ncbi.nlm.nih.gov/pubmed/37156393 http://dx.doi.org/10.1016/j.jbi.2023.104382 |
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