Automated Compliance Verification in ATM Using Principles from Ontology Matching

A. Vennesland, J. Gorman, S. Wilson, B. Neumayr, C. Schütz
Venn18a (2018)
Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018), Volume 2: KEOD, September 18-20, 2018, Seville, Spain, SciTePress, ISBN 978-989-758-330-8, 2018, pp. 39-50, The publication received Best Paper Award, 2018.
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Abstract (English)

Compliance with standard information models in diverse and complex domains such as Air Traffic Management is an important but highly challenging task. The challenges stem from the fact that the information models are often extensive, the diversity of the domain leads to diverging terminology, and the manual mapping of information elements necessary to assess compliance is very labor-intensive. This work proposes ways in which compliance verification techniques, currently based on manual techniques, can be supported and partly automated by means of a set of basic ontology matching techniques. We have evaluated these techniques in an experiment involving seven datasets consisting of various ATM ontologies that have been transformed to OWL from their original UML representations. A comparative analysis with two other state-of-the-art matching systems shows that some of our proposed matching techniques obtain good quality alignments, especially when they are combined using simple strategies. The evaluation also reveals that identifying equivalence relations is a far easier task than identifying other types of semantic relations.