Título:	Exploring a large language model for transforming taxonomic data into OWL: lessons learned and implications for ontology development.
Autoria:	SOARES, F. M. SARAIVA, A. M. PIRES, L. F. SANTOS, L. O. B. da S. MOREIRA, D. de A. CORRÊA, F. E. BRAGHETTO, K. R. DRUCKER, D. P. DELBEM, A. C. B.
Afiliação:	FILIPI MIRANDA SOARES, UNIVERSIDADE DE SÃO PAULO, UNIVERSITY OF TWENTE ANTONIO MAURO SARAIVA, UNIVERSIDADE DE SÃO PAULO LUÍS FERREIRA PIRES, UNIVERSITY OF TWENTE LUIZ OLAVO BONINO DA SILVA SANTOS, UNIVERSITY OF TWENTE, LEIDEN UNIVERSITY DILVAN DE ABREU MOREIRA, UNIVERSIDADE DE SÃO PAULO FERNANDO ELIAS CORRÊA, UNIVERSIDADE DE SÃO PAULO KELLY ROSA BRAGHETTO, UNIVERSIDADE DE SÃO PAULO DEBORA PIGNATARI DRUCKER, CNPTIA ALEXANDRE CLÁUDIO BOTAZZO DELBEM, UNIVERSIDADE DE SÃO PAULO.
Ano de publicação:	2025
Referência:	Data Intelligence, 2025.
Conteúdo:	Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becomes increasingly difficult when dealing with thousands of scientific names. To address this issue, this paper investigates the use of ChatGPT-4 to automate the development of the Organism module in the Agricultural Product Types Ontology (APTO) for species classification. Our methodology involved leveraging ChatGPT-4 to extract data from the GBIF Backbone API and generate OWL files for further integration in APTO. Two alternative approaches were explored: (1) issuing a series of prompts for ChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4 to design a Python algorithm to perform analogous tasks. Both approaches rely on a prompting method where we provide instructions, context, input data, and an output indicator. The first approach showed scalability limitations, while the second approach used the Python algorithm to overcome these challenges, but it struggled with typographical errors in data handling. This study highlights the potential of Large language models like ChatGPT-4 to streamline the management of species names in ontologies. Despite certain limitations, these tools offer promising advancements in automating taxonomy-related tasks and improving the efficiency of ontology development.
Thesagro:	Agricultura Taxonomia
NAL Thesaurus:	Agriculture Taxonomy Artificial intelligence
Palavras-chave:	ChatGPT Inteligência artificial Gráfico do conhecimento Ontologia Knowledge graph Ontology
Digital Object Identifier:	https://doi.org/10.3724/2096-7004.di.2025.0020
Notas:	On-line first.
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)