This paper introduces an ontology learning pipeline that leverages compact open-source LLMs (7B-32B parameters) to extract and structure knowledge from domain-specific industrial text. The approach utilizes reusable prompting strategies and open knowledge bases to handle the scarcity of annotated resources and complex ontology engineering workflows. It was validated on a private French corpus of power-grid incident reports, demonstrating effective extraction, enrichment, and evaluation capabilities.
- Open-source LLMs (7B-32B) can effectively drive ontology construction for niche domains.
- Reusable prompting strategies reduce the need for extensive labeled training data.
- Pipeline supports full lifecycle: extraction, structuring, enrichment, and evaluation.
- Validated on industrial power-grid incident reports, proving domain adaptability.
- Locally deployable models ensure data privacy for sensitive corporate corpora.
