📄 中文摘要
机场运营的文档化过程因技术术语复杂、法规严格、区域信息专有以及多个利益相关者之间的沟通碎片化而显得尤为复杂。这些数据孤岛和语义不一致性对全机场管理（TAM）倡议构成了重大障碍。研究提出了一种方法论框架，通过符号知识工程（KE）与生成性大型语言模型（LLM）的双阶段融合，构建一个基于领域的、机器可读的知识图谱（KG）。该框架采用了一个支架式融合策略，利用专家策划的KE结构指导LLM提示，从而促进语义一致知识的发现。
半自动化知识工程与全机场管理的流程映射
📄 English Summary
Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management
The documentation of airport operations is inherently complex due to extensive technical terminology, stringent regulations, proprietary regional information, and fragmented communication among multiple stakeholders. These data silos and semantic inconsistencies pose significant challenges to the Total Airport Management (TAM) initiative. A methodological framework is proposed for constructing a domain-grounded, machine-readable Knowledge Graph (KG) through a dual-stage fusion of symbolic Knowledge Engineering (KE) and generative Large Language Models (LLMs). This framework employs a scaffolded fusion strategy where expert-curated KE structures guide LLM prompts to facilitate the discovery of semantically aligned knowledge.