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

arXiv: https://arxiv.org/abs/2607.16201 · HTML: https://mlsanity.com/paper/2607.16201/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 10 of 10)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs](https://mlsanity.com/paper/2410.02811/) (2024)
- [End-to-End Ontology Learning with Large Language Models](https://mlsanity.com/paper/2410.23584/) (2024)
- [LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models](https://mlsanity.com/paper/2504.00752/) (2025)
- [LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challenge](https://mlsanity.com/paper/2409.10146/) (2024)
- [CEO: Corpus-based Open-Domain Event Ontology Induction](https://mlsanity.com/paper/2305.13521/) (2023)
- [Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies)](https://mlsanity.com/paper/2508.19428/) (2025)
- [Schemex: Discovering Design Patterns from Examples through Iterative Abstraction and Refinement](https://mlsanity.com/paper/2502.15105/) (2025)

## Abstract (arXiv)

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.