Prompting large language models based on semantic schema for text-to-Cypher transformation towards domain Q&A

Wan, Yuwei, Chen, Zheyuan, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Chen, Chong and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2025. Prompting large language models based on semantic schema for text-to-Cypher transformation towards domain Q&A. Decision Support Systems 199 , 114553. 10.1016/j.dss.2025.114553

PDF - Published Version Available under License Creative Commons Attribution Non-commercial. Download (3MB)

Abstract

Translating natural language inquiries into executable Cypher queries (text-to-Cypher) is a persistent bottleneck for non-technical teams relying on knowledge graphs (KGs) in fast-changing industrial settings. Rule and template converters need frequent updates as schemas evolve, while supervised and fine-tuned parsers require recurring training. This study proposes a schema-guided prompting approach, namely text-to-Cypher with semantic schema (T2CSS), to align large language models (LLMs) with domain knowledge for producing accurate Cypher. T2CSS distils a domain ontology into a lightweight semantic schema and uses adaptive filtering to inject the relevant subgraph and essential Cypher rules into the prompt for constraining generation and reducing schema-agnostic errors. This design keeps the prompt focused and within context length limits while providing the necessary domain grounding. Comparative experiments demonstrate that T2CSS with GPT-4 outperformed baseline models and achieved 86 % accuracy in producing correct Cypher queries. In practice, this study reduces retraining and maintenance effort, shortens turnaround times, and broadens KG access for non-experts.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0167-9236
Date of First Compliant Deposit:	20 October 2025
Date of Acceptance:	4 October 2025
Last Modified:	20 Oct 2025 12:45
URI:	https://orca.cardiff.ac.uk/id/eprint/181762

Actions (repository staff only)

Edit Item