![[2602.18497] PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18497] PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking
Summary
PIPE-RDF presents a novel pipeline for generating schema-specific NL-SPARQL benchmarks, enhancing RDF knowledge graph querying for enterprises by ensuring high validity and operational readiness.
Why It Matters
As enterprises increasingly rely on RDF knowledge graphs for data management, the PIPE-RDF pipeline addresses the gap in existing benchmarks that do not cater to proprietary schemas. This innovation supports better data querying and operational efficiency, crucial for businesses leveraging AI and data science.
Key Takeaways
- PIPE-RDF generates tailored NL-SPARQL benchmarks for enterprises.
- The pipeline achieves high validity rates, ensuring reliable data querying.
- Structured artifacts and operational metrics are provided for practical deployment.
Computer Science > Databases arXiv:2602.18497 (cs) [Submitted on 15 Feb 2026] Title:PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking Authors:Suraj Ranganath View a PDF of the paper titled PIPE-RDF: An LLM-Assisted Pipeline for Enterprise RDF Benchmarking, by Suraj Ranganath View PDF HTML (experimental) Abstract:Enterprises rely on RDF knowledge graphs and SPARQL to expose operational data through natural language interfaces, yet public KGQA benchmarks do not reflect proprietary schemas, prefixes, or query distributions. We present PIPE-RDF, a three-phase pipeline that constructs schema-specific NL-SPARQL benchmarks using reverse querying, category-balanced template generation, retrieval-augmented prompting, deduplication, and execution-based validation with repair. We instantiate PIPE-RDF on a fixed-schema company-location slice (5,000 companies) derived from public RDF data and generate a balanced benchmark of 450 question-SPARQL pairs across nine categories. The pipeline achieves 100% parse and execution validity after repair, with pre-repair validity rates of 96.5%-100% across phases. We report entity diversity metrics, template coverage analysis, and cost breakdowns to support deployment planning. We release structured artifacts (CSV/JSONL, logs, figures) and operational metrics to support model evaluation and system planning in real-world settings. Code is available at this https URL. Comments: Subjects: Databases (cs.DB); Artificial Intelligence (cs...