[2510.21045] From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL

Computer Science > Artificial Intelligence arXiv:2510.21045 (cs) [Submitted on 23 Oct 2025 (v1), last revised 29 Mar 2026 (this version, v3)] Title:From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL Authors:Ali Khosravi Kazazi, Zhenlong Li, M. Naser Lessani, Guido Cervone View a PDF of the paper titled From Questions to Queries: An AI-powered Multi-Agent Framework for Spatial Text-to-SQL, by Ali Khosravi Kazazi and 3 other authors View PDF Abstract:The complexity of SQL and the spatial semantics of PostGIS create barriers for non-experts working with spatial data. Although large language models can translate natural language into SQL, spatial Text-to-SQL is more error-prone than general Text-to-SQL because it must resolve geographic intent, schema ambiguity, geometry-bearing tables and columns, spatial function choice, and coordinate reference system and measurement assumptions. We introduce a multi-agent framework that addresses these coupled challenges through staged interpretation, schema grounding, logical planning, SQL generation, and execution-based review. The framework is supported by a knowledge base with programmatic schema profiling, semantic enrichment, and embedding-based retrieval. We evaluated the framework on the non-spatial KaggleDBQA benchmark and on SpatialQueryQA, a new multi-level and coverage-oriented benchmark with diverse geometry types, workload categories, and spatial operations. On KaggleDBQA, the system reache...