This article introduces a novel approach to SPARQL query construction, combining the exploration of a knowledge base in a tabular format with a domain ontology to guide the user. This approach addresses challenges faced by scientists who are not experts in knowledge bases when exploring complex and large-scale datasets. While SPARQL allows expressive queries over RDF graphs, formulating them remains difficult for users. Our solution preserves the tabular representation familiar in data science while leveraging an ontology to guide the definition of columns within the tabular view. The system generates relevant data samples, enabling users to iteratively refine their queries while, maintaining system responsiveness. We applied our approach to the OntoSIDES health education knowledge base.