Knowledge engineering is a discipline that focuses on the creation and
maintenance of processes that generate and apply knowledge. Traditionally,
knowledge engineering approaches have focused on knowledge expressed in formal
languages. The emergence of large language models and their capabilities to
effectively work with natural language, in its broadest sense, raises questions
about the foundations and practice of knowledge engineering. Here, we outline
the potential role of LLMs in knowledge engineering, identifying two central
directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2)
enabling knowledge engineering in natural language. Additionally, we formulate
key open research questions to tackle these directions.

知识工程学是一个专注于生成和应用知识的学科，传统上它主要关注以形式语言表达的知识。然而，大语言模型的出现及其与自然语言的高效处理能力，对知识工程学的基础和实践提出了问题。在这篇论文中，我们提出大语言模型在知识工程学中的潜在作用，并确定两个中心方向：1）创建混合的神经符号知识系统；2）实现自然语言下的知识工程。此外，我们还提出了一些关键的开放性研究问题来解决这些方向。