his paper presents an automated approach for building ontologies to improve treat-
ment recommendations for tuberculosis (TB), in particular multidrug-resistant tuberculosis (MDR-
TB) 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 prop-
erties, 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.

Tuberculosis · Generative AI · Ontologies · Medical Decision Support Systems · Text-to-Ontology