Text-to-OWL: Automated Ontology Construction for Tuberculosis Treatment Recommendation using Generative AI

his paper presents an automated approach for building ontologies to improve treatment recommendations for tuberculosis (TB), in particular multidrug-resistant tuberculosis (MDRTB) cases in Burkina Faso, using generative language models such as GPT-3. The aim is to facilitate the personalization of treatments according to the patient profile and drug resistance. Two approaches were explored: a automated approach based on the DaVinci GPT-3 model to generate OWL axioms from natural language sentences and a semi-automated approach using text extraction and natural language processing (NLP) techniques. The automated approach was fine-tuned with a dataset consisting of technical guidelines on TB management. The automated approach created an ontology composed of 158 classes, 55 object properties and 57 data properties, outperforming the semi-automated approach in terms of efficiency and accuracy. The axioms generated were validated using Protégé and integrated into a formal knowledge base. The study demonstrates that the use of language models such as GPT-3 can efficiently automate ontology generation, reducing human intervention.This approach is particularly well-suited to the management of complex MDR-TB cases and paves the way for standardization of treatment recommendations, while remaining adaptable to local specificities.