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Managing scientific names in ontologies that represent species taxonomies ischallenging due to the ever-evolving nature of these taxonomies. Manuallymaintaining these names becomes increasingly difficult when dealing withthousands of scientific names. To address this issue, this paper investigatesthe use of ChatGPT-4 to automate the development of the :Organism module in theAgricultural Product Types Ontology (APTO) for species classification. Ourmethodology involved leveraging ChatGPT-4 to extract data from the GBIFBackbone API and generate OWL files for further integration in APTO. Twoalternative approaches were explored: (1) issuing a series of prompts forChatGPT-4 to execute tasks via the BrowserOP plugin and (2) directing ChatGPT-4to design a Python algorithm to perform analogous tasks. Both approaches relyon a prompting method where we provide instructions, context, input data, andan output indicator. The first approach showed scalability limitations, whilethe second approach used the Python algorithm to overcome these challenges, butit struggled with typographical errors in data handling. This study highlightsthe potential of Large language models like ChatGPT-4 to streamline themanagement of species names in ontologies. Despite certain limitations, thesetools offer promising advancements in automating taxonomy-related tasks andimproving the efficiency of ontology development.

Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development