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AI Research
arXiv CS.AI·Jun 9·5 min read
PIPE-Cypher: Automatic Enterprise Benchmark Generation for Text-to-Cypher Systems
TL;DR
Enterprise property graphs have diverse structures and user interactions, making it challenging to create relevant benchmarks for Text-to-Cypher systems. PIPE-Cypher was developed to automatically generate benchmarks that reflect real user queries and graph entities.
✦ Why It Matters
Engineers can leverage PIPE-Cypher to create more relevant benchmarks for testing Text-to-Cypher systems in diverse enterprise environments.
Key Takeaways
- Utilize PIPE-Cypher for generating benchmarks.
- Ensure queries reflect real user interactions.
- Maintain diversity in generated queries.
- Adapt benchmarks to unique enterprise schemas.
Full Summary
Enterprise property graphs, which represent data in a graph format, often differ significantly in their schema structures and user interaction patterns. PIPE-Cypher is a tool designed to automatically generate benchmarks for Text-to-Cypher systems, which convert natural language queries into Cypher queries for graph databases.
The methodology involves analyzing existing graph schemas and user queries to create a set of natural language (NL) queries paired with executable Cypher queries. The benchmarks produced by PIPE-Cypher maintain diversity and balance, ensuring they reflect the actual questions users ask.
Results indicate that the generated benchmarks can effectively represent various enterprise environments, facilitating better testing and evaluation of Text-to-Cypher systems. This advancement allows engineers to create more relevant and effective graph-based applications.
Overall, PIPE-Cypher enhances the benchmarking process, making it more aligned with real-world usage.