用大模型将控制工程文本转为可读可算的知识图谱
LLM-Supported Formal Knowledge Representation for Enhancing Control Engineering Content with an Interactive Semantic Layer
- 基于PyIRK框架，用大模型自动转化自然语言与公式为形式化知识
- 构建交互式语义层，提升技术文档的知识传递效率
- 适合关注知识管理与智能工程的科研人员
控制工程领域研究产出激增，亟需新方法对领域知识进行结构化与形式化。本文提出一种基于大语言模型的半自动化方法，实现兼具人类可读性与机器可解释性的形式化知识表示，增强表达力。基于命令式知识表示（PyIRK）框架，我们展示了如何利用语言模型将自然语言描述和数学定义（以LaTeX源码形式提供）转化为形式化知识图谱。作为首个应用，我们构建了“交互式语义层”，用于增强原始文档，促进知识迁移。从我们的视角看，这有助于实现控制工程领域易获取、可协作、可验证的知识库愿景。
原文摘要 · Abstract (English)
The rapid growth of research output in control engineering calls for new approaches to structure and formalize domain knowledge. This paper briefly describes an LLM-supported method for semi-automated generation of formal knowledge representations that combine human readability with machine interpretability and increased expressiveness. Based on the Imperative Representation of Knowledge (PyIRK) framework, we demonstrate how language models can assist in transforming natural-language descriptions and mathematical definitions (available as LaTeX source code) into a formalized knowledge graph. As a first application we present the generation of an ``interactive semantic layer'' to enhance the source documents in order to facilitate knowledge transfer. From our perspective this contributes to the vision of easily accessible, collaborative, and verifiable knowledge bases for the control engineering domain.
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