Abstract
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.
Abstract (translated)
机场运营文档的编制具有内在复杂性，这源于其广泛的技术术语、严格的法规体系、专有的区域信息以及跨多方利益相关者的分散化沟通。由此产生的数据孤岛与语义不一致现象，对全域机场管理（TAM）倡议构成了重大障碍。本文提出了一种方法论框架，通过符号知识工程（KE）与生成式大语言模型（LLMs）的双阶段融合，构建基于领域 grounded 的可机器读取知识图谱（KG）。该框架采用支架式融合策略，由专家策划的KE结构指导LLM提示词，以促进语义对齐的知识三元组发现。我们在Google LangExtract库上评估了该方法，并通过对比基于本地化片段的推理与文档级处理，研究了上下文窗口利用的影响。与之前关于LLM长上下文性能衰减的实证观察相反，文档级处理改善了非线性程序依赖关系的恢复。为确保机场运营所需的高保真溯源，所提框架融合了概率发现模型与确定性算法，将每次提取锚定至其原始数据源。这确保了绝对的可追溯性与可验证性，弥合了"黑箱"生成式输出与运营工具所需透明度之间的差距。最后，我们引入了一个自动化框架，将这一流程操作化，从而从非结构化文本语料库中综合复杂运营工作流。
URL
https://arxiv.org/abs/2603.26076
