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

TL;DR

This paper introduces a semi-automated framework combining Knowledge Engineering and Large Language Models to create a transparent, domain-specific Knowledge Graph for airport management, improving procedural understanding and traceability.

Contribution

The novel dual-stage fusion approach leverages KE structures to guide LLMs, enhancing semantic alignment and provenance in knowledge extraction for airport operations.

Findings

Document-level LLM processing improves procedural dependency recovery.

The framework ensures high-fidelity provenance and traceability.

Automated synthesis of operational workflows from unstructured text.

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.