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.

翻译：本体知识库在结构化领域特定知识方面发挥着至关重要的作用，是构建有效知识管理系统的基础。然而，其传统的手动开发方式在可扩展性、一致性和适应性方面带来了重大挑战。生成式人工智能，特别是大型语言模型的最新进展，为自动化和增强本体知识库的开发提供了有前景的解决方案。本文提出了一种结构化的迭代方法，利用大型语言模型来优化知识获取、自动化本体构件生成，并实现持续的优化循环。我们通过在汽车销售领域开发用户情境画像本体的详细案例研究，展示了该方法。主要贡献包括显著加速本体构建过程、提升本体一致性、有效缓解偏差，以及增强本体工程过程的透明度。我们的研究结果凸显了将大型语言模型集成到本体开发中的变革潜力，特别是在提升知识管理系统的可扩展性、集成能力和整体效率方面。