AI-Driven Semantic Processing of Notices to Air Missions for Aeronautical Information Management: Ontology-Grounded Type-Aware Extraction

Abstract

Timely and accurate interpretation of Notices to Air Missions (NOTAMs) is essential for effective aeronautical information management (AIM). Although NOTAMs follow a standardized format, their abbreviated textual content and heterogeneous operational semantics make automated processing difficult. Existing methods often address type classification, information extraction, and semantic representation independently, leaving a gap between textual interpretation and ontology-grounded information management. To address this gap, this paper presents NOTE, an AI-driven ontology-grounded type-aware extraction framework for NOTAMs. NOTE is supported by a dynamic semantic ontology that combines a shared semantic module with seven type-specific sub-ontologies. Guided by the predicted semantic type, the framework activates the corresponding ontology-aligned schema contract, extracts and validates the relevant information, and materializes the result as Resource Description Framework (RDF) triples. Experiments on 25,341 operational NOTAMs and a manually curated ontology-mapping set produced a macro-F1 of 0.9424 for type-aware routing and an F1 of 0.9053 for structured extraction, with a schema conformance rate of 0.9976. The dynamic ontology achieved slot- and relation-level F1 scores of 0.9472 and 0.9835, respectively. Compared with a scale-matched static ontology, type-specific activation improved relation mapping by 13.06 percentage points while producing semantic richness close to the human-annotated reference. These findings indicate that NOTE provides an effective connection between abbreviated NOTAM text and validated ontology-grounded representations for AIM.