Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1176502
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dc.contributor.authorSOARES, F. M.eng
dc.contributor.authorSARAIVA, A. M.eng
dc.contributor.authorPIRES, L. F.eng
dc.contributor.authorSANTOS, L. O. B. da S.eng
dc.contributor.authorMOREIRA, D. de A.eng
dc.contributor.authorCORRÊA, F. E.eng
dc.contributor.authorBRAGHETTO, K. R.eng
dc.contributor.authorDRUCKER, D. P.eng
dc.contributor.authorDELBEM, A. C. B.eng
dc.date.accessioned2025-06-09T14:48:05Z-
dc.date.available2025-06-09T14:48:05Z-
dc.date.created2025-06-09
dc.date.issued2025
dc.identifier.citationData Intelligence, 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1176502-
dc.descriptionManaging 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectChatGPT
dc.subjectInteligência artificial
dc.subjectGráfico do conhecimento
dc.subjectOntologia
dc.subjectKnowledge graph
dc.subjectOntology
dc.titleExploring a large language model for transforming taxonomic data into OWL: lessons learned and implications for ontology development.
dc.typeArtigo de periódico
dc.subject.thesagroAgricultura
dc.subject.thesagroTaxonomia
dc.subject.nalthesaurusAgriculture
dc.subject.nalthesaurusTaxonomy
dc.subject.nalthesaurusArtificial intelligence
dc.description.notesOn-line first.eng
riaa.ainfo.id1176502
riaa.ainfo.lastupdate2025-06-09
dc.identifier.doihttps://doi.org/10.3724/2096-7004.di.2025.0020
dc.contributor.institutionFILIPI MIRANDA SOARES, UNIVERSIDADE DE SÃO PAULO, UNIVERSITY OF TWENTEeng
dc.contributor.institutionANTONIO MAURO SARAIVA, UNIVERSIDADE DE SÃO PAULOeng
dc.contributor.institutionLUÍS FERREIRA PIRES, UNIVERSITY OF TWENTEeng
dc.contributor.institutionLUIZ OLAVO BONINO DA SILVA SANTOS, UNIVERSITY OF TWENTE, LEIDEN UNIVERSITYeng
dc.contributor.institutionDILVAN DE ABREU MOREIRA, UNIVERSIDADE DE SÃO PAULOeng
dc.contributor.institutionFERNANDO ELIAS CORRÊA, UNIVERSIDADE DE SÃO PAULOeng
dc.contributor.institutionKELLY ROSA BRAGHETTO, UNIVERSIDADE DE SÃO PAULOeng
dc.contributor.institutionDEBORA PIGNATARI DRUCKER, CNPTIAeng
dc.contributor.institutionALEXANDRE CLÁUDIO BOTAZZO DELBEM, UNIVERSIDADE DE SÃO PAULO.eng
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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