Graph Query Generation with Constraint-guided Large Language Agents
Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and property graphs underexplored, despite increasing demand for unified KGQA in industry settings. We propose UniQGen, a novel constraint-based framework that employs LLM agents to dynamically extract and refine representative graph query clauses into executable, intent-aligned graph queries across query languages. The foundation of our method is a variant of Chase & Backchase, a family of algorithms for query optimization and reformulation. We extend Chase & Backchase with a dynamic reasoning process over query constraints that also interact with...
| Authors | Mengying Wang, Nicolaas Jedema, Rahul Pandey et al. |
|---|---|
| Primary category | cs.DB |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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