GS-SQL: Modeling Spatial Semantics in Spatial Text-to-SQL

Xu Zhang, Feiyang Xiao, Liang Yan, Zhiqing Zhang

Abstract: Conventional Text-to-SQL research tackles the problem of solving user questions in natural language by generating the corresponding SQL queries. Most of the recent works are dedicated to improving model’s robustness and generalizability in cross-domain settings. However, model’s capability in solving geography-related questions remains unexploited. In this paper we propose GS-SQL, a new framework that jointly model the schema item alignment and geospatial semantics in the question. The proposed framework consists of an improved abstract syntax tree for representing spatial queries, a novel spatial entity tagging module for locating entities in the question, and a spatial semantics extraction module for determining the spatial relationship between the entities. Then we propose GeoSpatialSpider, a dataset that introduces geospatial queries, requiring model to yield spatial functions and nested SQL inside functions. Finally we evaluate the proposed method on our dataset Experimental results show the effectiveness of our abstract syntax tree and GS-SQL in parsing geospatial semantics while preserving traditional Text-to-SQL capabilities.

External IDs:dblp:conf/ijcnn/ZhangXYZ24