用多智能体协作生成更规范的本体，提升可扩展性与可审计性。
Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach
- 分角色协作：领域专家、管理者、编码员、质检员各司其职，分工驱动生成
- 结构质量显著提升，查询能力小幅增强，关键在前期规划阶段发力
- 适合需要高可信度本体的金融、医疗等专业领域自动化构建
从非结构化自然语言自动构建正式本体仍是知识工程的核心挑战。尽管大语言模型（LLM）展现出潜力，但其架构设计如何影响生成质量仍不清晰，现有方法为何失败亦未明。本文以保险合同为具体领域，开展受控实验研究。首先建立单智能体基线，识别出本体设计模式不符、结构冗余及无效迭代修复等主要失败模式。随后提出多智能体架构，将本体构建分解为四个由产出物驱动的角色：领域专家、管理者、编码员和质量保证者。通过异构LLM评审评估架构质量，结合基于检索增强生成的互补评估与基于能力问题的SPARQL测试评估功能性可用性。结果表明，多智能体方法显著提升结构质量，适度增强查询能力，提升主要源于前置规划。研究揭示‘先规划、后生成’的产物驱动路径是实现可扩展自动化本体工程的可行方向。
原文摘要 · Abstract (English)
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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