  There is increasing evidence that question-answering (QA) systems with Large
Language Models (LLMs), which employ a knowledge graph/semantic representation
of an enterprise SQL database (i.e. Text-to-SPARQL), achieve higher accuracy
compared to systems that answer questions directly on SQL databases (i.e.
Text-to-SQL). Our previous benchmark research showed that by using a knowledge
graph, the accuracy improved from 16% to 54%. The question remains: how can we
further improve the accuracy and reduce the error rate? Building on the
observations of our previous research where the inaccurate LLM-generated SPARQL
queries followed incorrect paths, we present an approach that consists of 1)
Ontology-based Query Check (OBQC): detects errors by leveraging the ontology of
the knowledge graph to check if the LLM-generated SPARQL query matches the
semantic of ontology and 2) LLM Repair: use the error explanations with an LLM
to repair the SPARQL query. Using the chat with the data benchmark, our primary
finding is that our approach increases the overall accuracy to 72% including an
additional 8% of "I don't know" unknown results. Thus, the overall error rate
is 20%. These results provide further evidence that investing knowledge graphs,
namely the ontology, provides higher accuracy for LLM powered question
answering systems.
