用大模型从文档中自动构建通用知识图谱骨架，覆盖率达95%以上。
Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models
- 基于大模型生成文档对应的实体、属性与关系结构
- 在4类不同领域数据上结构覆盖率高达95%-100%
- 适合需要快速构建知识体系的跨领域应用
知识工程仍是高智能系统的关键瓶颈。现有自动化方法或依赖预设模式、局限于特定领域，或输出无结构内容，难以用于下游流程。我们提出生成式本体归纳（GOI），一种无需领域先验的框架，能从文档语料中生成包含实体、维度、属性、关系及约束的生成蓝图，并以六种节点类型、七种边类型的形式导出为YAML/JSON格式。我们引入节点覆盖率评分，衡量生成结果中结构节点（类、属性、维度）的覆盖比例。在四个差异显著的本体——熟悉的软件服务发票模式、自定义职位描述本体、保密的疼痛管理就诊记录本体和专业服务合同与工作说明书本体——上的控制生成验证显示，GOI生成覆盖了全部结构主干，达95%-100%；而通用三字段模板在发票本体上为97.8%，但在职位描述本体上降至52.2%，疼痛管理本体为62.2%，合同本体为78.3%。结构覆盖率不受模型对文档类型的熟悉度影响。
原文摘要 · Abstract (English)
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.
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