Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 20, 2026Transactions in GIS

Text‐to‐SpatialSQL: A LLM Based Method for Generating Spatial SQL Queries With Geo‐Knowledge Extracted From Software User Manual

View Full Paper
Ask AI
Bookmark
Share

Authors

YMYicheng MaYZYifan ZhangWZWenbo Zhang

Discussion

Loading...

Member takes

Overview

Randomized trial demonstrates improved spatial SQL query generation in non-expert users, suggesting better accessibility.

Key Points

  • This research aims to enhance spatial SQL query generation for non-expert users by utilizing large language models and domain-specific knowledge.
  • Integrates Retrieval-Augmented Generation with large language models
  • Extracts knowledge from spatial database documentation
  • Employs prompt engineering to create standardized spatial SQL queries.
  • Significant improvement in execution accuracy (EX) demonstrated
  • Improved component matching rate (CM) across various schema complexities
  • Efficacy validated using benchmark datasets.

Cite This Study

Ma et al. (2026) studied this question.

synapsesocial.com/papers/6a3630f5db0793dc1a537f95https://doi.org/10.1111/tgis.70312
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Natural Language Query Transformation Method for Spatial Databases Based on Large Language Model2026
  2. 2Domain-specific SQL generation with LLMs: A hybrid framework combining knowledge graphs and retrieval-augmentation2026 · 2 citations
  3. 3An Automated Framework for Natural Language-Based Spatial Query Using Large Language Models2026 · 2 citations
  4. 4Intent-Driven Hybrid Semantic–Spatial Retrieval–Augmented Generation for Intelligent Prospecting with GIS Visualization2026
  5. 5Retrieval Augmented Generation for Relative Distance-based Spatial Reasoning2026