Abstract
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.
Abstract (translated)
本体知识库（OKB）在构建特定领域的知识结构方面发挥着重要作用，并为有效的知识管理系统奠定了基础。然而，传统的手动开发方式面临着可扩展性、一致性和适应性的重大挑战。近期，在生成式人工智能领域，尤其是大型语言模型（LLM），为自动化和增强OKB的开发提供了有前景的解决方案。本文介绍了一种利用LLM进行优化的知识获取、自动本体文件生成以及持续改进周期的结构化迭代方法。通过详细案例研究车辆销售领域的用户上下文配置文件本体开发，我们展示了这种方法的应用。 关键贡献包括： - 显著加快了本体构建过程。 - 提高了本体的一致性。 - 有效地减轻偏差。 - 增强了本体工程过程中透明度。 我们的研究成果强调了将LLM集成到本体开发中的变革潜力，特别是在知识管理系统中提高了可扩展性、集成能力和总体效率。
URL
https://arxiv.org/abs/2601.10436
