Retrieval-Augmented Generation of Ontologies from Relational Databases

• URL: http://arxiv.org/abs/2506.01232v1
• Date: Mon, 02 Jun 2025 01:10:05 GMT
• Title: Retrieval-Augmented Generation of Ontologies from Relational Databases
• Authors: Mojtaba Nayyeri, Athish A Yogi, Nadeen Fathallah, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab,
• Abstract summary: We present RIGOR, Retrieval-augmented Iterative Generation of RDB Ontologies.<n>An approach that turns relationals into rich schemas with minimal human effort is presented.
• Score: 13.160850863758
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Transforming relational databases into knowledge graphs with enriched ontologies enhances semantic interoperability and unlocks advanced graph-based learning and reasoning over data. However, previous approaches either demand significant manual effort to derive an ontology from a database schema or produce only a basic ontology. We present RIGOR, Retrieval-augmented Iterative Generation of RDB Ontologies, an LLM-driven approach that turns relational schemas into rich OWL ontologies with minimal human effort. RIGOR combines three sources via RAG, the database schema and its documentation, a repository of domain ontologies, and a growing core ontology, to prompt a generative LLM for producing successive, provenance-tagged delta ontology fragments. Each fragment is refined by a judge-LLM before being merged into the core ontology, and the process iterates table-by-table following foreign key constraints until coverage is complete. Applied to real-world databases, our approach outputs ontologies that score highly on standard quality dimensions such as accuracy, completeness, conciseness, adaptability, clarity, and consistency, while substantially reducing manual effort.

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