发表机构
Tursio（Tursio）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对关系数据库自然语言查询中查询上下文构建耗时且存在冷启动的问题，提出自动生成查询上下文的方法，通过查询流生成数据模型并用加权采样实例化，已在50多个真实数据库上部署。
AI 中文摘要
关系数据库是业务应用程序的系统记录，并且通过自然语言界面查询它们的需求日益增长。一个关键挑战是，AI模型需要适当的查询上下文，即与相应数据模型片段配对的示例问题，以生成准确的SQL。如今，创建这种上下文是一个手动且耗时的过程，需要同时具备SQL和数据库模式的专业知识，这导致新数据库面临冷启动问题，并且随着模式和查询模式的演变，持续维护负担沉重。我们提出了一种为关系数据库自动生成查询上下文的方法。我们的方法定义了捕获数据检索和分析问题常见模式的查询流，通过遍历这些流系统地生成数据模型，并使用加权策略从数据库中采样的特定值进行实例化，以最大化多样性和覆盖范围。生成的问答对（问题-数据模型对）可用于指导自然语言界面准确查询关系数据库。我们报告了在Tursio连接的50多个真实世界数据库中部署此方法的经验。
英文摘要
Relational databases are the systems of record for business applications, and there is growing demand to query them through natural language interfaces. A key challenge is that AI models need appropriate query context, i.e., sample questions paired with their corresponding data-model fragments, to generate accurate SQL. Today, creating this context is a manual, time-consuming process that requires expertise in both SQL and the database schema, leading to a cold start problem for new databases and an ongoing maintenance burden as schemas and query patterns evolve. We present an automated approach for generating query context for relational databases. Our method defines query flows that capture common patterns of data retrieval and analysis questions, systematically generates data models by traversing these flows, and instantiates them with specific values sampled from the database using weighted strategies that maximize diversity and coverage. The resulting question--data-model pairs can be used to guide natural language interfaces in accurately querying relational databases. We report on our experience deploying this approach across more than 50 real-world databases connected to Tursio.
