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Natural Language Knowledge Graph Query Execution: Leveraging Controlled Semantics in the LLM Context Window

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and examples. We show that for database queries, data model concepts pass to LLMs more effectively through representations whose vocabulary terms carry declared, machine-readable semantics (controlled semantics). NLKGQ is a working system and reusable framework that does this for data modeled in a knowledge graph. A formal OWL ontology serves as the transfer mechanism, concentrating the meaning of the data into semantically precise tokens the model can

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