Derivation of Optimized Threshold of Semantic Alignment Metrics for Intepretation of Interoperability and Reusability of Cross-enterprise Vehicle Service Interface Models (Informatica, 2023)
Over the past decade, cars have turned gradually into real cyber physical systems. The collaboration of services between the service-oriented, cross-enterprise vehicle application frameworks has increased to generate novel, smart and complicated vehicle services. Consequently, from an interoperability perspective, semantically mapping of vehicle service component’s interface ontological models emerged as a big research interest in automotive application domain that manipulates several cross-enterprise synergy knowledge applications frameworks. The ontological metamodeling lays the foundation for building semantic bridge and exploring semantic associations between service components’ interface models based on the domain knowledge for semantic interoperability. Also, several semantic quality metrics has been defined over time for the vehicle service interface ontological metamodels. The empirically evaluated values of these metrics can be used to assess progress in cross-enterprise interoperability between the service and the clients’ APIs ontological models in vehicle domain. Despite potential benefits of semantic alignment quality metrics, the effective use of these metrics for vehicle service interface ontologies have proven elusive. Yes, such metrics can be used successfully for quantification, but then they mostly fail to provide adequate annotations in subsequent decision-making in the direction of semantic interoperability and reusability. The effective use of ontology semantic alignment quality metrics is basically hindered in the absence of the meaningful thresholds. In fact, the absence of an effective and meaningful threshold for the semantic similarity measure between various vehicle service interface ontological metamodels, motivates this research work which not only proposes a design approach to an optimized threshold for the semantic similarity metrics but also applies this threshold on few defined semantic alignment quality metrics. This paper also uses a real-world vehicle domain industrial case study to illustrate the design approach.