## 5 Test Case Scenario and Discussion
To better describe and explain the process of ontology metamorphosis and generation of complex outputs, a conceptual architecture for semantic lifting [57] was adopted (Figure 9). This architecture is intended to provide the necessary basis for ensuring interoperability among model elements and ontology concepts. Figure 8: Schema of payload of the getMappingSuggestion service. Figure 9: Metamorphosis of ontologies and output building. The conceptual architecture contains three scales: the first reflects the syntax and semantic annotations used. The input model for the getMappingSuggestion service, containing origin and target data models, follows JSON formatting, with an internal semantic developed for the semantic management component to be consumed by other vf-OS framework components, including the data integrator. Ontologies that can be imported by the semantic management component are of two main types: RDF/XML extended with OWL syntax and Turtle. The metamodel layer represents the mapping of the imported ontology with origin and target data models. In the modeling layer, the initial ontology is modified in consideration with the origin and target models. Moreover, the output model is generated while combining both input models and modified ontology. Resulting suggestions contain the first node acquired from origin model, the last node from the target model, and the sequence of the in-between nodes. These nodes can be acquired both from the input models and modified ontology, where all new concepts, if any, are already incorporated. The whole process is represented in Figure 9.  
The first column in Figure 10 identifies the origin and target concepts (i.e., labels between the established mappings). For this specific case, one of the origin concepts has been chosen, namely the \"_Emergency Stop_\" actuating device, with appropriate suggestions that are presented in the target model of Figure 8. The second column is used to represent the Cypher queries generated to query the result. The concepts in the graph are marked as follows: (i) blue concepts were extracted from origin data model; (ii) green concepts were from the target data model; and (iii) grey concepts were extracted from imported ontology. The records in Figure 10 represent suggestions after the mapping process. Figure 10 also represents how the suggestions for the same origin and target concepts are changed if the relations among intermediary concepts evolve. As it can be seen, the number of intermediary nodes increased, though the distance or semantic distance between the nodes \"_Emergency Stop_\" and \"_Assembly_\" decreased, making the new suggestion preferable. As an example, the \"_Emergency Stop_\" concept from the input model is chosen. The resulting graph containing all 5 concepts from the origin data model is  
\[G_{\textit{res}}=(\textit{Manufacturing},\textit{Conveyor},\textit{Emergency Stop},\textit{Company\_X,\textit{Lisbon}}), \tag{16}\]  
Figure 10: Illustrative results. and the subgraph containing the suggestion for \"_Emergency Stop_\" is  
\[\textit{``Emergency Stop\"}=\big{(}V_{\textit{pw}},\ E_{\textit{pw}},\ w_{\textit{pw}} \big{)}, \tag{17}\]  
where \(V_{\textit{pw}}\) is a set containing all the vertices forming the resulting graph based on four suggestions (Figure 10):  
\[ \begin{array}{l}V\_pw=\{\textit{Emergency Stop,Conveyor,Manufacturing,Assembly,Device},\\ \textit{AcutatingDevice,CoffeeMachine,SensingDevice,Sensor,Speed Monitor},\\ \textit{HasDomainOfInterest,DomainOfInterest,Transportation,Company\_Y}.\end{array}  \tag{18}\]  
\(E_{\textit{pw}}\) is a set containing all the edges between vertices in the path forming the suggestions:  
\[