Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.

翻译：知识图谱（KG）为我们提供了一种结构化、灵活、透明、跨系统且协作的方式，用于组织社会各领域以及工业与科学学科中的知识与数据。在有效性方面，KG超越了任何其他表现形式。然而，知识图谱工程（KGE）需要深入掌握图结构、网络技术、现有模型与词汇表、规则集、逻辑以及最佳实践等知识，同时还需要大量的工作投入。考虑到近年来大语言模型（LLM）及其接口与应用的发展，我们与ChatGPT进行了全面实验，以探索其在支持KGE方面的潜力。本文选取了部分实验及其结果进行展示，以说明ChatGPT如何协助我们开发与管理知识图谱。