Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach

• URL: http://arxiv.org/abs/2604.23090v1
• Date: Sat, 25 Apr 2026 00:58:17 GMT
• Title: Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach
• Abstract summary: Large language models (LLMs) show promise, but it remains unclear which architectural design choices drive generation quality.<n>We present a controlled experimental study using domain-specific insurance contracts to investigate these questions.<n>Results show that the multi-agent approach significantly improves structural quality and modestly enhances queryability.
• Score: 1.029029220360574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language models (LLMs) show promise, it remains unclear which architectural design choices drive generation quality and why current approaches fail. We present a controlled experimental study using domain-specific insurance contracts to investigate these questions. We first establish a single-agent LLM baseline, identifying key failure modes such as poor Ontology Design Pattern compliance, structural redundancy, and ineffective iterative repair. We then introduce a multi-agent architecture that decomposes ontology construction into four artifact-driven roles: Domain Expert, Manager, Coder, and Quality Assurer. We evaluate performance across architectural quality (via a panel of heterogeneous LLM judges) and functional usability (via competency question driven SPARQL evaluation with complementary retrieval augmented generation based assessment). Results show that the multi-agent approach significantly improves structural quality and modestly enhances queryability, with gains driven primarily by front-loaded planning. These findings highlight planning-first, artifact-driven generation as a promising and more auditable path toward scalable automated ontology engineering.

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