发表机构
Pivots Global（Pivots全球公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对本体工程瓶颈，提出领域无关的生成式本体归纳框架GOI，从文档语料库归纳生成蓝图并输出类型化图，引入节点覆盖分数评估，实验表明该方法能高比例覆盖结构主干，且覆盖率不受文档类型熟悉度影响。
AI 中文摘要
本体工程仍然是知识密集型人工智能系统中的一个关键瓶颈。现有的自动化方法要么依赖预定义模式，要么在狭窄领域内运行，要么产生不适合下游管道的非结构化输出。我们引入了生成式本体归纳（GOI），这是一个领域无关的框架，它从示例语料库中归纳出一个生成蓝图——实体、维度、属性、关系和约束，并将其作为YAML/JSON格式的类型化图（六种节点类型，七种边类型）输出。我们引入了节点覆盖分数，这是一种新的评估指标，用于衡量生成输出中出现的结构本体节点（类、属性和维度）的比例。对四个对比本体进行的受控生成验证表明，GOI提示生成在每种情况下都覆盖了95%-100%的结构主干；一个通用的三字段模板在发票模式上的准确率为97.8%，但在工作描述本体上降至52.2%，在疼痛管理本体上为62.2%，在专业服务合同本体上为78.3%。无论文档类型对模型有多熟悉，结构覆盖率都保持不变。
英文摘要
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.
Comments10 pages, 2 figures, 1 table. LNCS format
