让AI理解跨多个图数据库的复杂查询，自动拆解并整合结果。
Toward Multi-Database Query Reasoning for Text2Cypher
- 提出三阶段框架：找库、拆问、跨库融合
- 支持多数据库间信息联合查询，突破单库限制
- 适合需要跨系统查图数据的开发者和分析师
大型语言模型显著提升了自然语言到数据库的接口能力，其中Text2Cypher专注于将用户问题转化为可执行的图数据库Cypher查询，使用户无需掌握查询语言即可访问图数据。现有Text2Cypher系统通常假设仅存在一个预选的图数据库，查询基于已知模式生成。然而，现实系统常分布于多个独立的图数据库中，相关信息可能跨越多个来源。为解决这一局限，本文提出从单数据库查询生成转向多数据库查询推理。系统需推理（i）相关数据库，（ii）如何在多个数据库间分解问题，以及（iii）如何整合部分结果。为此，我们通过三阶段路线图形式化该设置：数据库路由、多数据库分解、跨数据库类型与查询语言的异构查询推理。本工作为Text2Cypher提供了多数据库推理的结构化框架，并识别出源选择、查询分解与结果整合中的关键挑战，旨在支持更真实、可扩展的图数据库自然语言接口。
原文摘要 · Abstract (English)
Large language models have significantly improved natural language interfaces to databases by translating user questions into executable queries. In particular, Text2Cypher focuses on generating Cypher queries for graph databases, enabling users to access graph data without query language expertise. Most existing Text2Cypher systems assume a single preselected graph database, where queries are generated over a known schema. However, real-world systems are often distributed across multiple independent graph databases organized by domain or system boundaries, where relevant information may span multiple sources. To address this limitation, we propose a shift from single-database query generation to multi-database query reasoning. Instead of assuming a fixed execution context, the system must reason about (i) relevant databases, (ii) how to decompose a question across them, and (iii) how to integrate partial results. We formalize this setting through a three-phase roadmap: database routing, multi-database decomposition, and heterogeneous query reasoning across database types and query languages. This work provides a structured formulation of multi-database reasoning for Text2Cypher and identifies challenges in source selection, query decomposition, and result integration, aiming to support more realistic and scalable natural language interfaces to graph databases.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。