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This paper presents a self-improving 'researcher agent' for Text-to-SPARQL question answering over knowledge graphs, which iteratively refines its own prompts and tools. Evaluated on DBpedia, it achieves 0.22 accuracy and identifies predicate selection as the main bottleneck.
This paper proposes GGC, a Generator–Gate–Corrector framework that selectively corrects LLM-generated SPARQL queries to improve reliability and accuracy, achieving 98.33% query-level accuracy on MCQA while reducing inference overhead by 45%.
This paper introduces Kontrast, a framework for automatically detecting knowledge inconsistencies across Wikipedia text, tables, and Wikidata knowledge graphs using Text-to-SPARQL and LLM reasoning.