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Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID
preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248388/ https://www.ncbi.nlm.nih.gov/pubmed/35776071 http://dx.doi.org/10.1093/database/baac048 |
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author | Langnickel, Lisa Darms, Johannes Heldt, Katharina Ducks, Denise Fluck, Juliane |
author_facet | Langnickel, Lisa Darms, Johannes Heldt, Katharina Ducks, Denise Fluck, Juliane |
author_sort | Langnickel, Lisa |
collection | PubMed |
description | preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has demanded dynamic development. Whereas new semantic concepts have been added over time—such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest—the service has been also extended with several features improving the usability and user friendliness. Most importantly, the user is now able to give feedback on detected semantic concepts, i.e. a user can mark annotations as true positives or false positives. In addition, we expanded our methods to construct search queries. The presented version of preVIEW also includes links to the peer-reviewed journal articles, if available. With the described system, we participated in the BioCreative VII interactive text-mining track and retrieved promising user-in-the-loop feedback. Additionally, as the occurrence of long-term symptoms after an infection with the virus SARS-CoV-2—called long COVID—is getting more and more attention, we have recently developed and incorporated a long COVID classifier based on state-of-the-art methods and manually curated data by experts. The service is freely accessible under https://preview.zbmed.de |
format | Online Article Text |
id | pubmed-9248388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92483882022-07-05 Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID Langnickel, Lisa Darms, Johannes Heldt, Katharina Ducks, Denise Fluck, Juliane Database (Oxford) Original Article preVIEW is a freely available semantic search engine for Coronavirus disease (COVID-19)-related preprint publications. Currently, it contains >43 800 documents indexed with >4000 semantic concepts, annotated automatically. During the last 2 years, the dynamic situation of the corona crisis has demanded dynamic development. Whereas new semantic concepts have been added over time—such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest—the service has been also extended with several features improving the usability and user friendliness. Most importantly, the user is now able to give feedback on detected semantic concepts, i.e. a user can mark annotations as true positives or false positives. In addition, we expanded our methods to construct search queries. The presented version of preVIEW also includes links to the peer-reviewed journal articles, if available. With the described system, we participated in the BioCreative VII interactive text-mining track and retrieved promising user-in-the-loop feedback. Additionally, as the occurrence of long-term symptoms after an infection with the virus SARS-CoV-2—called long COVID—is getting more and more attention, we have recently developed and incorporated a long COVID classifier based on state-of-the-art methods and manually curated data by experts. The service is freely accessible under https://preview.zbmed.de Oxford University Press 2022-07-01 /pmc/articles/PMC9248388/ /pubmed/35776071 http://dx.doi.org/10.1093/database/baac048 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Langnickel, Lisa Darms, Johannes Heldt, Katharina Ducks, Denise Fluck, Juliane Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title | Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title_full | Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title_fullStr | Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title_full_unstemmed | Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title_short | Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID |
title_sort | continuous development of the semantic search engine preview: from covid-19 to long covid |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9248388/ https://www.ncbi.nlm.nih.gov/pubmed/35776071 http://dx.doi.org/10.1093/database/baac048 |
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