# Retrieval-Augmented Generation of Ontologies from Relational Databases

arXiv: https://arxiv.org/abs/2506.01232 · HTML: https://mlsanity.com/paper/2506.01232/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 37 of its 95 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 30 of 37)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Teaching Algorithmic Reasoning via In-context Learning](https://mlsanity.com/paper/2211.09066/) (2022)
- [RelBench: A Benchmark for Deep Learning on Relational Databases](https://mlsanity.com/paper/2407.20060/) (2024)
- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- ["We Need Structured Output": Towards User-centered Constraints on Large Language Model Output](https://mlsanity.com/paper/2404.07362/) (2024)
- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [Agent-OM: Leveraging LLM Agents for Ontology Matching](https://mlsanity.com/paper/2312.00326/) (2023)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models](https://mlsanity.com/paper/2412.15235/) (2024)
- [L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://mlsanity.com/paper/2309.17446/) (2023)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering](https://mlsanity.com/paper/2308.16622/) (2023)
- [End-to-End Ontology Learning with Large Language Models](https://mlsanity.com/paper/2410.23584/) (2024)
- [An Experiment in Retrofitting Competency Questions for Existing Ontologies](https://mlsanity.com/paper/2311.05662/) (2023)

## Built on by (most cited first, 4 of 4)

- [Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Management](https://mlsanity.com/paper/2602.00029/) (2026)
- [Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale](https://mlsanity.com/paper/2604.09608/) (2026)
- [SINT-Flow: Schema Integration using Large Language Model Workflows](https://mlsanity.com/paper/2607.24492/) (2026)
- [Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems](https://mlsanity.com/paper/2604.20795/) (2026)

## Abstract (arXiv)

Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning. Existing approaches either require substantial expert effort or produce shallow ontologies that reflect the logical schema structure but fail to fully capture domain semantics. We present RIGOR (Retrieval-augmented Iterative Generation of RDB Ontologies), an LLM-driven pipeline that converts relational schemas into semantically rich OWL2DL ontologies with minimal human intervention. For each relational table, RIGOR generates a direct mapping to guarantee schema coverage, then enriches it via retrieval from three sources: relational schema context and documentation, external domain ontologies, and an ontology that grows incrementally as each validated fragment is integrated. A Gen-LLM produces provenance-annotated ontology fragments (delta ontologies), which are validated and, when needed, corrected by an independent Judge-LLM before integration. Guided by foreign-key constraints, the process iterates over relational tables until the full schema is covered. Experiments across three databases spanning two domains show that RIGOR consistently outperforms baseline methods across standard quality metrics while requiring no human oversight.