Intelligent filtering and querying of Digital NOtices To AirMen (NOTAM)

Intelligent filtering and querying of Digital NOtices To AirMen (NOTAM) has been identified as important but is still an unsolved issue in terms of fully exploiting the potential in future Air Traffic Management. The research project “semNOTAM” uses a knowledge-based approach exploiting semantic technologies for tackling the problem of efficient, flexible and context-aware filtering and querying of NOTAMs. Notices to airmen provide timely knowledge which is essential to personnel concerned with flight operations.

Nowadays NOTAM-systems are still based on teletype technology leading to insufficiently structured text messages which are difficult to filter. Pilots, as a consequence, complain about excessively long pre-flight bulletins, information overload, lack of support on what NOTAMs actually mean, lack of priorisation by space and time, lack of situation-awareness and temporal relevance, and lack of control over how, when and what information is presented. The introduction of Digital NOTAM will provide information in a much more structured way, but will still require rules and filtering functions to provide the information in an efficient way to the end user.

The semNOTAM approach extends semantic technologies for fine-grained event-type, location-, time-, and aircraft-aware filtering and querying of Digital NOTAMs. Semantic systems are characterised to differentiate between a declarative (usually logic-based) knowledge representation of data and business rules of a domain and processing engines that control the knowledge or query extensional data of the knowledge base. They are thus in line with the move to the data-centric nature of Aeronautical Information Management (AIM) from the product-centric nature of Aeronautical Information Services (AIS) and in addition they provide a semantic description of data and business rules. By offering declarative specifications independent of use, semantic systems simplify interoperability and applications (as these can rely on and [re-]use concept definitions of the knowledge base.)

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