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EPPO ontology: a semantic-driven approach for plant and pest codes representation

The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In th...

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Autores principales: Ayllón-Benitez, Aarón, Bernabé-Diaz, José Antonio, Espinoza-Arias, Paola, Esnaola-Gonzalez, Iker, Beeckman, Delphine S. A., McCaig, Bonnie, Hanzlik, Kristin, Cools, Toon, Castro Iragorri, Carlos, Palacios, Nicolás
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10315572/
https://www.ncbi.nlm.nih.gov/pubmed/37404339
http://dx.doi.org/10.3389/frai.2023.1131667
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author Ayllón-Benitez, Aarón
Bernabé-Diaz, José Antonio
Espinoza-Arias, Paola
Esnaola-Gonzalez, Iker
Beeckman, Delphine S. A.
McCaig, Bonnie
Hanzlik, Kristin
Cools, Toon
Castro Iragorri, Carlos
Palacios, Nicolás
author_facet Ayllón-Benitez, Aarón
Bernabé-Diaz, José Antonio
Espinoza-Arias, Paola
Esnaola-Gonzalez, Iker
Beeckman, Delphine S. A.
McCaig, Bonnie
Hanzlik, Kristin
Cools, Toon
Castro Iragorri, Carlos
Palacios, Nicolás
author_sort Ayllón-Benitez, Aarón
collection PubMed
description The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.
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spelling pubmed-103155722023-07-04 EPPO ontology: a semantic-driven approach for plant and pest codes representation Ayllón-Benitez, Aarón Bernabé-Diaz, José Antonio Espinoza-Arias, Paola Esnaola-Gonzalez, Iker Beeckman, Delphine S. A. McCaig, Bonnie Hanzlik, Kristin Cools, Toon Castro Iragorri, Carlos Palacios, Nicolás Front Artif Intell Artificial Intelligence The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work. Frontiers Media S.A. 2023-06-19 /pmc/articles/PMC10315572/ /pubmed/37404339 http://dx.doi.org/10.3389/frai.2023.1131667 Text en Copyright © 2023 Ayllón-Benitez, Bernabé-Diaz, Espinoza-Arias, Esnaola-Gonzalez, Beeckman, McCaig, Hanzlik, Cools, Castro Iragorri and Palacios. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Ayllón-Benitez, Aarón
Bernabé-Diaz, José Antonio
Espinoza-Arias, Paola
Esnaola-Gonzalez, Iker
Beeckman, Delphine S. A.
McCaig, Bonnie
Hanzlik, Kristin
Cools, Toon
Castro Iragorri, Carlos
Palacios, Nicolás
EPPO ontology: a semantic-driven approach for plant and pest codes representation
title EPPO ontology: a semantic-driven approach for plant and pest codes representation
title_full EPPO ontology: a semantic-driven approach for plant and pest codes representation
title_fullStr EPPO ontology: a semantic-driven approach for plant and pest codes representation
title_full_unstemmed EPPO ontology: a semantic-driven approach for plant and pest codes representation
title_short EPPO ontology: a semantic-driven approach for plant and pest codes representation
title_sort eppo ontology: a semantic-driven approach for plant and pest codes representation
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10315572/
https://www.ncbi.nlm.nih.gov/pubmed/37404339
http://dx.doi.org/10.3389/frai.2023.1131667
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