## 5 Experimental Analysis and Implementation
### Evaluation and Discussion
For the processing of ontologies, the Jena [53] framework has been used and the method to mine the descriptions of ontology graphs (Section \"Procedure for Learning Representations\") has been utilized. Vectors of the ontology entities have been developed before model assignments. Training of AuEs model has taken up to 200 iterations in the first settings and up to 400 iterations in the second. The representation learning experiment with AuEs has been analyzed and the results are shown in the graph (Figure 24), expressing the precision, recall, and f-measure. Here to compare the alignment of the ontology graphs in the two settings of our approach, including Set-A and Set-B with different layers of AuEs. It has been realized that the method acquires reasonable value in f-measure by a maximum of 84% with the first set in Set-A, and by 86% maximum f-measure with the second set in Set-B. Additionally, we examine the discovery time concerning semantic virtual object ontology models. In the semantic interoperability implementation setup discussed in Section 4.2.1, these settings allow the discovery process of virtual and composite virtual objects, which is achieved through processing SPARQL query requests to the ontology catalog. In these experimental settings, it has also been observed that the discovery time is proportional to the increasing number of objects. As in the case of CVOs, the object's discovery processing require the reading of the ontology graphs which requires requesting RDF graphs from the SPARQL endpoints. The increasing number of requests for the CVOs  
Figure 23: Semantic ontology alignment accuracy score on affective states ontologies. increase the processing time to discover the CVOs as it can be observed from Figure 25a. Moreover, in the semantic interoperability provisioning settings, discovering the VO ontology graphs has also been realized which plays a part in semantic composition scenarios. In this case, an increase in VOs increases, ultimately the time required to discover the objects. However, it has also been realized from Figure 25b, that VOs are discovered in less time as of the CVOs, this is because of the fact that when the CVO discovery process is instantiated, additional rules need to be checked that involve checking the conditions on semantic ontology graphs and the semantic dependencies on VO ontologies. Besides, the system has also been evaluated with respect to semantic processing of microservices. The evaluation has been done by analyzing the execution time of different tasks of microservices' functional operations (as shown in Figure 26). These tasks include (1) the semantic annotation (SAN) microservice for the semantic annotation of the data, (2) the query processing (QPR) microservice which involves the discovery of multiple VOs and CVO ontologies, (3) the semantic alignment (SAL) microservice for semantic mapping of ontology models. For each microservice task, instances have been created to accept service requests. The average execution time of one and ten instances of each service has been illustrated in the following graph. The SAN execution time is less than the other microservices execution time, but it highly depends on the volume of data, in this case, 500+ tuples have been annotated by the service by a single instance. The QPR execution time, on the other hand, is dependent on the querying the SPARQL endpoint; the query processing time for an average discovery of 30 objects, has been shown for the single service instance. It has been analyzed that the SAL services require additional average execution time, it is due to fact that the alignment process includes sub-processes for the model selection and execution to perform alignment among the concepts or entities of the selected ontologies. Figure 24: Semantic ontology alignment accuracy on external data. Figure 25: (**a**) CVOs semantic ontologies discovery, (**b**) VOs semantic ontologies discovery.