# Enhancing Text2Cypher with Schema Filtering

arXiv: https://arxiv.org/abs/2505.05118 · HTML: https://mlsanity.com/paper/2505.05118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 28 of 28)

- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [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)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking](https://mlsanity.com/paper/2305.15066/) (2023)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)
- [RSL-SQL: Robust Schema Linking in Text-to-SQL Generation](https://mlsanity.com/paper/2411.00073/) (2024)
- [Global Reasoning over Database Structures for Text-to-SQL Parsing](https://mlsanity.com/paper/1908.11214/) (2019)
- [E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL](https://mlsanity.com/paper/2409.16751/) (2024)
- [Text2Cypher: Bridging Natural Language and Graph Databases](https://mlsanity.com/paper/2412.10064/) (2024)
- [Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL](https://mlsanity.com/paper/2501.12372/) (2025)
- [LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs](https://mlsanity.com/paper/2410.06062/) (2024)
- [SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task](https://mlsanity.com/paper/2406.10710/) (2024)
- [Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation](https://mlsanity.com/paper/2402.17144/) (2024)
- [Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL](https://mlsanity.com/paper/2402.16567/) (2024)
- [Assessing SPARQL capabilities of Large Language Models](https://mlsanity.com/paper/2409.05925/) (2024)
- [Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance](https://mlsanity.com/paper/2405.17076/) (2024)
- [Extractive Schema Linking for Text-to-SQL](https://mlsanity.com/paper/2501.17174/) (2025)
- [NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language](https://mlsanity.com/paper/2412.10434/) (2024)
- [Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation](https://mlsanity.com/paper/2502.12911/) (2025)
- [S2CTrans: Building a bridge from SPARQL to Cypher](https://mlsanity.com/paper/2304.00531/) (2023)
- [Rel2Graph: Automated Mapping From Relational Databases to a Unified Property Knowledge Graph](https://mlsanity.com/paper/2310.01080/) (2023)
- [SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL](https://mlsanity.com/paper/2409.10007/) (2024)
- [SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL](https://mlsanity.com/paper/2502.11438/) (2025)

## Built on by (most cited first, 6 of 6)

- [STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing](https://mlsanity.com/paper/2506.21575/) (2025)
- [Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval](https://mlsanity.com/paper/2601.16038/) (2026)
- [Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering](https://mlsanity.com/paper/2605.10318/) (2026)
- [CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment](https://mlsanity.com/paper/2606.04645/) (2026)
- [LLM+Graph@VLDB'2025 Workshop Summary](https://mlsanity.com/paper/2604.02861/) (2026)
- [What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework](https://mlsanity.com/paper/2609.12746/) (2026)

## Abstract (arXiv)

Knowledge graphs represent complex data using nodes, relationships, and properties. Cypher, a powerful query language for graph databases, enables efficient modeling and querying. Recent advancements in large language models allow translation of natural language questions into Cypher queries - Text2Cypher. A common approach is incorporating database schema into prompts. However, complex schemas can introduce noise, increase hallucinations, and raise computational costs. Schema filtering addresses these challenges by including only relevant schema elements, improving query generation while reducing token costs. This work explores various schema filtering methods for Text2Cypher task and analyzes their impact on token length, performance, and cost. Results show that schema filtering effectively optimizes Text2Cypher, especially for smaller models. Consistent with prior research, we find that larger models benefit less from schema filtering due to their longer context capabilities. However, schema filtering remains valuable for both larger and smaller models in cost reduction.