## 5 Experimental Analysis and Implementation
### Evaluation and Discussion  
To evaluate the semantic interoperability provisioning features with the proposed semantic mediation model, several aspects have been analyzed. First, the experiments have been performed to evaluate the semantic alignment process; this is achieved through analyzing the performance of the learning algorithm on the semantic data models. The experiments have been repeated and the best results have been reported. Secondly, experiments have also been performed to evaluate the behavior of semantic objects created in the process, such as VOs and CVOs. The performance of microservices processes for the semantic interoperability processing tasks, such as the discovery of VO ontologies from the global ontology catalog, semantic alignment, and query processing tasks have been analyzed. Based on the semantic alignment of ontology models, the final mapping of semantic concepts has also been evaluated. The mapping results are expressed in terms of the accuracy of the semantic similarity alignment of ontologies. A standard method of measuring the performance of the ontology alignment task has been followed. For this, a set of affective health state ontologies data have been utilized for the experiments. To judge the performance of semantic interoperability, the precision measure, recall, and f-measure are defined with the following equations, where \(RL_{\text{alignment}}\) are the relevant alignments and \(RT_{\text{alignment}}\) are the retrieved alignments. \[\text{{Precision Measure}}\ =\ P\ =\ \frac{1}{\text{n}}\ \sum_{i\ =\ 1}^{n}Pm_{i}\ =\ \frac{1}{\text{n}}\sum_{i\ =\ 1}^{n}\frac{\left|\left\{RL_{\text{alignment}}\right\}_{i}\ \cap\ \left\{RT_{\text{alignment}}\right\}_{i}\right|}{\left|\left\{RL_{\text{ alignment}}\right\}_{i}\right|} \tag{8}\]  
\[\text{{Recall Measure}}\ =\ R\ =\ \frac{1}{\text{n}}\ \sum_{i\ =\ 1}^{n}Rm_{i}\ =\ \frac{1}{\text{n}}\sum_{i\ =\ 1}^{n}\frac{\left|\left\{RL_{\text{alignment}}\right\}_{i}\ \cap\ \left\{RT_{\text{alignment}}\right\}_{i}\right|}{\left|\left\{RT_{\text{ alignment}}\right\}_{i}\right|} \tag{9}\]  
\[\text{{F-Measure}}\ =\ \frac{1}{\text{n}}\ \sum_{i\ =\ 1}^{n}\frac{\left\{2\cdot R_{i}\cdot P_{i} \right\}}{\left[R_{i}+\ P_{i}\right]} \tag{10}\]  
Figure 22: Semantic alignment of ontology models. Besides, results have been extracted, preprocessed, and prepared based on various tools. For the development of ontology models, protege has been used. To extract different ontology features, Jena Libraries [53] have been utilized. For experiments, we have set up Apache Jena Fuseki [59] based SPARQL Endpoint and RDF repositories. The learning models have been implemented using Python with the Keras. Training has been performed on Intel Core i7; with 3.4 GHz clocked, 32 GB RAM and NVIDIA GeForce GTX configuration. The experimental analysis for the semantic alignment task has been performed on several ontology pairs discussed in Section 4.2.2. These ontology pairs constitute the data related to affective human health conditions. We divide the ontologies into two groups where the first group is used for training, and the second group is utilized for the testing purpose. In the experiment settings, reference ontology from the source group needs to be aligned with the ontologies in the target group. In these experiments, an F-Measure which is known as the measure of test the accuracy has been calculated. It can be realized from Figure 23, that the testing accuracy obtained on different data is reasonably good such as 80.24%, 82.66%, 85.95% in the three groups shown below, respectively. Moreover, experimental analysis on the benchmark dataset of ontologies from Ontology Alignment Evaluation Initiative (O.A.E.I) [45; 64] has been conducted. The dataset constitutes about 110 ontologies. We utilize 70% of the data for training and 30% for testing. In the experiment settings, the source ontology needs to be aligned with the target ontologies.