S2CTrans: Building a bridge from SPARQL to Cypher
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Abstract (arXiv)
In graph data applications, data is primarily maintained using two models: RDF (Resource Description Framework) and property graph. The property graph model is widely adopted by industry, leading to property graph databases generally outperforming RDF databases in graph traversal query performance. However, users often prefer SPARQL as their query language, as it is the W3C's recommended standard. Consequently, exploring SPARQL-to-Property-Graph-Query-Language translation is crucial for enhancing graph query language interoperability and enabling effective querying of property graphs using SPARQL. Despite the substantial differences in semantic representation and processing logic between SPARQL and property graph query languages like Cypher, this paper demonstrates the feasibility of translating SPARQL to Cypher for graph traversal queries using graph relational algebra. We present the S2CTrans framework, which achieves SPARQL-to-Cypher translation while preserving the original semantics. Experimental results with the Berlin SPARQL Benchmark (BSBM) datasets show that S2CTrans successfully converts most SELECT queries in the SPARQL 1.1 specification into type-safe Cypher statements, maintaining result consistency and improving the efficiency of data querying using SPARQL.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2024Text2Cypher: Bridging Natural Language and Graph Databasescited by 30
- 2025Enhancing Text2Cypher with Schema Filteringcited by 6
- 2025Omne-R1: Learning to Reason with Memory for Multi-hop Question Answeringcited by 1
- 2026Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)cited by 0
- 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturingcited by 0
- 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Frameworkcited by 0
- 2026What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework11 Sep 2026
- 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing1 Sep 2026
- 2026Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)12 Jun 2026
- 2025Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering24 Aug 2025
- 2025Enhancing Text2Cypher with Schema Filtering8 May 2025
- 2024Text2Cypher: Bridging Natural Language and Graph Databases13 Dec 2024