cs.AIMar 27, 2026

Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management

Authors: Darryl Teo, Adharsha Sam, Chuan Shen Marcus Koh, Rakesh Nagi, Nuno Antunes Ribeiro

Organizations: Aviation Studies Institute, Singapore University of Technology and Design, Singapore

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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.

Figures & tables

Fig. 1: Knowledge Engineering Architecture for A-CDM Milestones
Fig. 2: Protégé Airport Operations Class Schema
Fig. 3: LangExtract Prompt and Few-shot Examples
Fig. 4: Swimlane Diagram for the Airport Collaborative Decision-Making Process
Fig. 5: LangExtract Structured Extraction Output With Source Reference
 Short-ContextLong-Context
TP440442
FP1815
FN138
Precision ( P )0.9610.967
Recall ( R )0.9710.982
F1 Score ( F1 )0.9660.975
TABLE I: Performance Information Retrieval Metrics of LangExtract on the EUROCONTROL A-CDM Manual
 Short-ContextLong-Context
 FPTPFPTP
MATCH_EXACT41383149
MATCH_FUZZY1427312276
MATCH_LESSER022017
TABLE II: Provenance Information Retrieval Metrics of LangExtract on the EUROCONTROL A-CDM Manual
Fig. 6: Sample video analytics of Haneda Airport Operations
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