GS-SQL: Modeling Spatial Semantics in Spatial Text-to-SQL
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".