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NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation

BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and comp...

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Autores principales: Martínez-Romero, Marcos, Jonquet, Clement, O’Connor, Martin J., Graybeal, John, Pazos, Alejandro, Musen, Mark A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5463318/
https://www.ncbi.nlm.nih.gov/pubmed/28592275
http://dx.doi.org/10.1186/s13326-017-0128-y
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author Martínez-Romero, Marcos
Jonquet, Clement
O’Connor, Martin J.
Graybeal, John
Pazos, Alejandro
Musen, Mark A.
author_facet Martínez-Romero, Marcos
Jonquet, Clement
O’Connor, Martin J.
Graybeal, John
Pazos, Alejandro
Musen, Mark A.
author_sort Martínez-Romero, Marcos
collection PubMed
description BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. METHODS: We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. RESULTS: Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. CONCLUSIONS: Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender, and via a Web service API). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13326-017-0128-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-54633182017-06-08 NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation Martínez-Romero, Marcos Jonquet, Clement O’Connor, Martin J. Graybeal, John Pazos, Alejandro Musen, Mark A. J Biomed Semantics Research BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. METHODS: We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. RESULTS: Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. CONCLUSIONS: Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender, and via a Web service API). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13326-017-0128-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-07 /pmc/articles/PMC5463318/ /pubmed/28592275 http://dx.doi.org/10.1186/s13326-017-0128-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Martínez-Romero, Marcos
Jonquet, Clement
O’Connor, Martin J.
Graybeal, John
Pazos, Alejandro
Musen, Mark A.
NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title_full NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title_fullStr NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title_full_unstemmed NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title_short NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
title_sort ncbo ontology recommender 2.0: an enhanced approach for biomedical ontology recommendation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5463318/
https://www.ncbi.nlm.nih.gov/pubmed/28592275
http://dx.doi.org/10.1186/s13326-017-0128-y
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