Graph Query Generation with Constraint-guided Large Language Agents
📰 ArXiv cs.AI
Learn to generate graph queries with constraint-guided large language agents for unified Knowledge Graph Question Answering (KGQA)
Action Steps
- Build a UniQGen framework using LLM agents to generate graph queries
- Configure the framework to handle constraint-based query generation
- Apply the framework to property graphs and Cypher queries
- Test the generated queries for intent alignment and executability
- Compare the performance of UniQGen with existing KGQA systems
Who Needs to Know This
Data scientists and software engineers working on KGQA systems can benefit from this approach to improve query generation and handling of property graphs and Cypher queries
Key Insight
💡 Constraint-guided LLM agents can dynamically extract and refine graph query clauses for executable and intent-aligned queries
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🤖 Generate graph queries with constraint-guided LLM agents for unified KGQA! 📈
Key Takeaways
Learn to generate graph queries with constraint-guided large language agents for unified Knowledge Graph Question Answering (KGQA)
Full Article
Title: Graph Query Generation with Constraint-guided Large Language Agents
Abstract:
arXiv:2605.00845v1 Announce Type: cross Abstract: 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
Abstract:
arXiv:2605.00845v1 Announce Type: cross Abstract: 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
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