# SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases

arXiv: https://arxiv.org/abs/2505.18363 · HTML: https://mlsanity.com/paper/2505.18363/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 20 of 20)

- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows](https://mlsanity.com/paper/2411.07763/) (2024)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation](https://mlsanity.com/paper/2405.07467/) (2024)
- [Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing](https://mlsanity.com/paper/2301.07507/) (2023)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [RSL-SQL: Robust Schema Linking in Text-to-SQL Generation](https://mlsanity.com/paper/2411.00073/) (2024)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL](https://mlsanity.com/paper/2503.18596/) (2025)
- [PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency](https://mlsanity.com/paper/2403.09732/) (2024)
- [Large Language Model Enhanced Text-to-SQL Generation: A Survey](https://mlsanity.com/paper/2410.06011/) (2024)
- [Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph](https://mlsanity.com/paper/2208.03903/) (2022)
- [Extractive Schema Linking for Text-to-SQL](https://mlsanity.com/paper/2501.17174/) (2025)
- [Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL](https://mlsanity.com/paper/2412.12522/) (2024)
- [DBCopilot: Natural Language Querying over Massive Databases via Schema Routing](https://mlsanity.com/paper/2312.03463/) (2023)
- [SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation](https://mlsanity.com/paper/2310.18376/) (2023)

## Built on by (most cited first, 3 of 3)

- [Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics](https://mlsanity.com/paper/2606.28387/) (2026)
- [MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQL](https://mlsanity.com/paper/2608.09588/) (2026)
- [JoinGR: Learning to Traverse Join Graphs for Table Retrieval](https://mlsanity.com/paper/2610.01064/) (2026)

## Abstract (arXiv)

Text-to-SQL systems translate natural language questions into executable SQL queries, and recent progress with large language models (LLMs) has driven substantial improvements in this task. Schema linking remains a critical component in Text-to-SQL systems, reducing prompt size for models with narrow context windows and sharpening model focus even when the entire schema fits. We present a zero-shot, training-free schema linking approach that first constructs a schema graph based on foreign key relations, then uses a single prompt to Gemini 2.5 Flash to extract source and destination tables from the user query, followed by applying classical path-finding algorithms and post-processing to identify the optimal sequence of tables and columns that should be joined, enabling the LLM to generate more accurate SQL queries. Despite being simple, cost-effective, and highly scalable, our method achieves state-of-the-art results on the BIRD benchmark, outperforming previous specialized, fine-tuned, and complex multi-step LLM-based approaches. We conduct detailed ablation studies to examine the precision-recall trade-off in our framework. Additionally, we evaluate the execution accuracy of our schema filtering method compared to other approaches across various model sizes.