There is increasing evidence that question-answering (QA) systems with Large Language Models (LLMs), which employ a knowledge graph representation of an enterprise SQL database (Text-to-SPARQL), achieve higher accuracy compared to systems that answer questions directly on SQL databases (Text-to-SQL). The objective of this research is to further improve the accuracy of these LLM Question Answering systems. Our approach, Ontology-based Query Check (OBQC), is to check the LLM generated SPARQL query against the semantics specified by the ontology. A query will be flagged as incorrect and prevented from execution if it does not align with the ontological semantics. The study also explores the LLM’s capability in repairing a SPARQL query given an explanation of the error (LLM Repair). Our methods are evaluated using the chat with the data benchmark. The primary finding is our method further increases the accuracy overall by 21.59% thus pushing the overall accuracy level to 65.63%. These results provide further evidence that investing knowledge graphs, namely the ontology, provides higher accuracy for LLM powered question answering systems. Our method is a component of the data.world AI Context Engine which is being widely used by customers in Generative AI production use cases that enable business users to chat with SQL databases.