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-$F_1$ of 0.9424 for type-aware routing and an $F_1$ of 0.9053 for structured extraction, with a schema conformance rate of 0.9976. The dynamic ontology achieved slot- and relation-level $F_1$ 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.

Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable bitemporal states that support accurate and efficient current and historical access. To this end, this paper proposes BiTE, a bitemporal event-centered database framework that connects object-level event evidence, historical state versions, and materialized current-state projections. By integrating business and system time with NOTAM-specific lifecycle rules, BiTE supports state maintenance, historical reconstruction, and source traceability. A MongoDB-based prototype was evaluated using 44,591 NOTAMs from five major U.S. aerodromes. Independent manual validation showed 96.14–100% agreement across object identification and lifecycle-maintenance tasks. Across 1500 manually verified queries, BiTE achieved F1 scores of 99.43% and 98.66% for current-state and airport-overview retrieval, respectively, and a historical hit rate of 95.80%, outperforming representative message-oriented, relational-bitemporal, and RDF-based implementations. Mean query latency remained below 3.7 ms, while functionally equivalent ablations confirmed the performance contribution of the layered architecture. These results demonstrate that BiTE enables accurate, traceable, and efficient object-state management for dynamic aeronautical information.