SAMOA - A Semi-Automated Ontology Alignment Method for Systems Integration in Safety-Critical Environments

The integration of heterogeneous data sources with even heterogeneous semantic meanings poses a challenge for data and system integrators. Ontology Alignment (OA) tries to identify similarities between heterogeneous ontologies and to automatically create suitable mappings for transformation. However, the usage of standard OA approach for safety-critical domains needs further investigation.In this paper, we describe a semi-automated ontology alignment approach (SAMOA) well-suitable for integration scenarios of safety-critical applications. The major contribution of our approach is the modeling differentiation between individual system knowledge and generic domain-specific knowledge.We evaluate our approach by providing a typical use case example from the Air Traffic Management (ATM) domain. In addition we analyze to what extent the SAMOA approach can be supported by state-of-the-art OA approaches.