The Semantic Web relies on the use of ontologies to ensure data sharing, reuse, and interoperability, thereby representing knowledge comprehensible to comput ers.However, querying ontologies, often performed using the SPARQL language, becomes a challenge, especially for non-expert users. Barriers include linguistic challenges due to syntactic complexity, the need to understand the underlying ontology structure, potential errors in query formulation, and difficulty express ing complex queries. To make knowledge access more user-friendly, this article explores ontology querying in natural language. We propose a reflection aimed at guiding future domain designers in the interrogation of ontologies in natural language, orienting them in their choice of approach according to the applica tions they will develop. The study relies on the application of Natural Language Processing (NLP) techniques, integrating tools such as Owlready2, RDFLIB, Prot´ eg´ e2000, and the Python programming language to achieve its goals. Three distinct approaches were evaluated for this purpose. The first approach, scenario based, was tested on two separate ontologies: one related to university concepts and the other to estate settlement. This approach demonstrates remarkable adaptability across various ontologies. However, its effectiveness is closely linked to the types of scenarios and the specific jargon of the evaluated domain. The other two approaches, one based on SPARQL query patterns and the other on decision tree structure, were evaluated on a specific ontology, estate settlement. They show robust performance in terms of result accuracy. Nevertheless, their effectiveness depends on model training for named entity detection, node list 1 management, and enrichment of SPARQL query patterns, operating exclusively within this particular ontology.

Ontology, SPARQL language, Natural language processing, Query patterns, decision trees