PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
📰 ArXiv cs.AI
Learn how to generate automatic enterprise benchmarks for Text-to-Cypher systems using PIPE-Cypher, a novel approach to create deployment-relevant benchmarks
Action Steps
- Apply PIPE-Cypher to generate benchmarks for Text-to-Cypher systems
- Configure the system to reflect the unique schema structure and internal terminology of the enterprise property graph
- Test the generated benchmarks to ensure they are executable and use real graph values
- Use the benchmarks to evaluate and improve the performance of Text-to-Cypher systems
- Compare the results of different benchmarking approaches to determine the effectiveness of PIPE-Cypher
Who Needs to Know This
Data scientists and software engineers working on Text-to-Cypher systems can benefit from this approach to generate benchmarks that reflect real-world user interactions and graph structures
Key Insight
💡 PIPE-Cypher generates deployment-relevant benchmarks that reflect the questions users and agents actually ask of the graph
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🚀 Automatically generate enterprise benchmarks for Text-to-Cypher systems with PIPE-Cypher! 📈
Key Takeaways
Learn how to generate automatic enterprise benchmarks for Text-to-Cypher systems using PIPE-Cypher, a novel approach to create deployment-relevant benchmarks
Full Article
Title: PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
Abstract:
arXiv:2606.08481v1 Announce Type: cross Abstract: Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction patterns. A deployment-relevant Text2Cypher benchmark therefore reflects the questions users and agents actually ask of that graph. Creating such a benchmark is difficult because schemas and values are unique, and graph structure changes over time. Each NL-query pair must also be executable, use real g
Abstract:
arXiv:2606.08481v1 Announce Type: cross Abstract: Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction patterns. A deployment-relevant Text2Cypher benchmark therefore reflects the questions users and agents actually ask of that graph. Creating such a benchmark is difficult because schemas and values are unique, and graph structure changes over time. Each NL-query pair must also be executable, use real g
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