Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

A new framework called Generative Ontology Induction (GOI) achieves 95-100% structural coverage across four diverse ontologies, according to a preprint on arXiv. The domain-agnostic method induces a typed graph with six node types and seven edge types from document corpora using large language models, outperforming a generic three-field template that dropped to 52.2% on a job description ontology.

arXiv:2607.16201v1 Announce Type: new Abstract: Ontology engineering remains a critical bottleneck in knowledge-intensive AI systems. Existing automated approaches either depend on predefined schemas, operate within narrow domains, or produce unstructured outputs unsuitable for downstream pipelines. We introduce Generative Ontology Induction GOI , a domain-agnostic framework that induces a generative blueprint - entities, dimensions, properties, relationships, and constraints - from a corpus of examples and exports it as a typed graph six node types, seven edge types in YAML/JSON. We introduce the Node Coverage Score, a novel evaluation metric that measures the fraction of structural ontology nodes classes, properties, and dimensions appearing in generated outputs. A controlled generative validation on four contrasting ontologies - a familiar Software Services Invoice schema, a custom Job Description Ontology, a confidential Pain-Management Clinical Visit Record Ontology, and a Professional Services Contract & Statement of Work Ontology - shows that GOI-prompted generation covers 95-100% of the structural backbone in every case; a generic three-field template holds at 97.8% on the invoice schema but drops to 52.2% on the Job Description Ontology, 62.2% on the Pain-Management ontology, and 78.3% on the Professional Services Contract ontology. The structural coverage holds regardless of how familiar the document type is to the model.