用大模型自动构建领域知识库，提升效率与一致性。
Development of Ontological Knowledge Bases by Leveraging Large Language Models
- 通过迭代式流程利用大模型自动提取和生成知识结构
- 构建过程提速，且减少不一致和偏见问题
- 适合需要快速构建高质量知识库的团队使用
本研究提出一种基于大语言模型（LLMs）的系统化、迭代式方法，用于优化领域知识库（OKBs）的开发。传统人工构建方式存在可扩展性差、一致性低等问题。本文以汽车销售领域的用户情境画像知识库为例，展示如何利用大模型实现知识获取自动化、本体构件生成及持续优化。实验表明，该方法显著加速了知识库构建速度，提升了本体的一致性，有效缓解了潜在偏见，并增强了开发过程的透明度。结果证明，将大模型融入本体工程具有变革性潜力，能大幅提升知识管理系统的可扩展性、集成能力与整体效率。
原文摘要 · Abstract (English)
Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems. However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.
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