Abstract
Air traffic management (ATM) relies on the timely exchange
of information between stakeholders to ensure safety and efficiency of
air traffic operations. In an effort to achieve semantic interoperability
within ATM, the Single European Sky ATM Research (SESAR) program
has developed the ATM Information Reference Model (AIRM), which
individual information exchange models should comply with. An OWL
representation of the AIRM – the AIRM Ontology (AIRM-O) – facilitates applications. Independently from the European efforts, the NASA
Air Traffic Management Ontology (ATMONTO) has been developed as
an RDF/OWL ontology representing ATM concepts to facilitate data
integration and analysis in support of NASA aeronautics research. Conceptualization mismatches between the AIRM-O and ATMONTO ontologies
– mostly due to different design decisions, but also as a consequence of the
different regulatory systems and philosophies underlying ATM in Europe
and the United States – pose a challenge to automatic ontology matching
algorithms. In this paper, we describe mismatches between AIRM-O and
ATMONTO, evaluate performance of automatic matching systems over
these ontologies, and provide a manual reference alignment.
of information between stakeholders to ensure safety and efficiency of
air traffic operations. In an effort to achieve semantic interoperability
within ATM, the Single European Sky ATM Research (SESAR) program
has developed the ATM Information Reference Model (AIRM), which
individual information exchange models should comply with. An OWL
representation of the AIRM – the AIRM Ontology (AIRM-O) – facilitates applications. Independently from the European efforts, the NASA
Air Traffic Management Ontology (ATMONTO) has been developed as
an RDF/OWL ontology representing ATM concepts to facilitate data
integration and analysis in support of NASA aeronautics research. Conceptualization mismatches between the AIRM-O and ATMONTO ontologies
– mostly due to different design decisions, but also as a consequence of the
different regulatory systems and philosophies underlying ATM in Europe
and the United States – pose a challenge to automatic ontology matching
algorithms. In this paper, we describe mismatches between AIRM-O and
ATMONTO, evaluate performance of automatic matching systems over
these ontologies, and provide a manual reference alignment.