Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper presents a methodological framework 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). The framework employs a scaffolded fusion strategy in which expert-curated KE structures guide LLM prompts to facilitate the discovery of semantically aligned knowledge triples. We evaluate this methodology on the Google LangExtract library and investigate the impact of context window utilization by comparing localized segment-based inference with document-level processing. Contrary to prior empirical observations of long-context degradation in LLMs, document-level processing improves the recovery of non-linear procedural dependencies. To ensure the high-fidelity provenance required in airport operations, the proposed framework fuses a probabilistic model for discovery and a deterministic algorithm for anchoring every extraction to its ground source. This ensures absolute traceability and verifiability, bridging the gap between "black-box" generative outputs and the transparency required for operational tooling. Finally, we introduce an automated framework that operationalizes this pipeline to synthesize complex operational workflows from unstructured textual corpora.

翻译：机场运营文档因其包含大量专业术语、严格法规、区域性专有信息以及多方利益相关者间的碎片化沟通而具有内在复杂性。由此产生的数据孤岛与语义不一致性对机场全域管理（Total Airport Management, TAM）倡议构成了重大阻碍。本文提出了一种方法框架，通过符号化知识工程（Knowledge Engineering, KE）与生成式大语言模型（Large Language Models, LLMs）的双阶段融合，构建领域基底的机器可读知识图谱（Knowledge Graph, KG）。该框架采用脚手架式融合策略，由专家精选的KE结构引导LLM提示，以促进语义对齐的知识三元组发现。我们基于Google LangExtract库评估该方法，并通过对比局部化片段推理与文档级处理，研究上下文窗口利用的影响。与先前关于LLM长上下文退化的实证观察相反，文档级处理改善了非线性流程依赖关系的恢复。为确保机场运营所需的高保真溯源，所提框架融合了用于发现的概率模型与用于将每个抽取结果锚定至原始来源的确定性算法，从而保证绝对可追溯性与可验证性，弥合了“黑箱”生成输出与运营工具所需透明性之间的鸿沟。最后，我们引入一个自动化框架，将该流程实例化，以从非结构化文本语料库中合成复杂的操作工作流。