用大模型自动生成海洋领域概念层级，提升专业语料构建效率
Specific Domain Ontology Construction Using Large Language Models
- 让大模型扮演领域专家，基于初始概念自动构建概念层级
- 20个巴西海洋领域本体由GPT-3.5和GPT-4生成，整体结构连贯
- 结果需人工修正，适合需要快速构建初版知识体系的领域
本体是人类与系统均可理解的信息组织结构。但由于其手工构建费时费力，许多特定领域缺乏参考本体。大型语言模型（LLMs）在自然语言理解方面的卓越能力，激发了其在多个领域的应用，包括本体开发。本文实验了一种利用大模型作为领域专家的角色，为给定初始概念构建概念层次的方法。使用GPT-3.5和GPT-4为巴西海洋领土（又称蓝亚马逊）领域自动构建了20个本体，并由人类专家进行评估。结果显示，模型能够生成整体连贯的领域概念化表达，但所有输出均未达到无需精炼即可使用的完整程度。
原文摘要 · Abstract (English)
Ontologies are useful structures to organize and maintain information that can be understood both by humans and systems. However, since their manual crafting is a laborious task, many specific domains lack reference ontologies. The outstanding ability for understanding natural language demonstrated by the Large Language Models (LLMs) has motivated their application to aid on a variety of fields, including on ontology development. This work presents the experimentation with a technique that uses LLMs in the role of domain experts to build conceptual hierarchies for a given initial concept. Twenty ontologies automatically constructed for the domain of the Brazilian maritime territory (a.k.a the Blue Amazon) using GPT-3.5 and GPT-4 were then evaluated by human experts. The models were able to construct overall coherent conceptualizations of the domain, but none of the outputs was completely satisfactory as a representation of the context without refinement.
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