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...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.展开更多
基金the Fundação de AmparoàPesquisa do Estado de São Paulo(FAPESP)under grant numbers 21/15125-0 and 22/08385-8.K.R.B.received funding from FAPESP(Grant n.2023/00779-0)F.M.S.,D.P.D.,K.R.B.,F.E.C.,D.A.M.,A.C.B.D.,and A.M.S.thank the Center for Artificial Intelligence(C4AI),a partnership of USP,IBM,and FAPESP(Grant 2019/07665-4).
文摘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.
