## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models
#### 4.2.3 Semantic Ontology Alignment using Deep Representation Learning  
Ontology graphs are an important way to represent shared concepts in diverse domains for the sharing of knowledge across different systems. Even though ontologies that describe the same domain, may differ in their element level information, as they are designed by different knowledge engineers, which provide their viewpoint while encoding the semantic descriptions and relationships on real-world concepts. The solution to semantic interoperability is to provide an effective ontology alignment approach. The existing ontology alignment approaches are based on diverse types of mapping mechanisms, some apply the character level mapping while others make use of tokens information of the entities in a  
Figure 6: Base ontology model conceptualization. chosen ontology. Some feature extraction-based approaches have also been explored in the recent literature [45; 46; 47]. However, these studies are very limited, and they only focused on extracting features that are based on the ontology structure or terminology only. However, engineering features from these information sources are quite time-consuming. As well, such features could only be used for the domain they have been designed for; therefore, the performance of ontology alignment methods varies according to the features used. To resolve such problems, deep learning-based approaches have been introduced in the literature [48; 49]. In these approaches, the deep neural network [50] based models are employed to generate a representation from ontology descriptions which could be used to compute similarity among ontology entities. To realize the semantic alignment process, we follow a similar method and provide interoperability provision through ontology mapping for heterogeneous data in different VO ontology models. In our approach, we extend utilization not only on the VO ontology classes and properties but also the VO ontology individual description and their metadata information. We have considered the processing of ontology data, such as extracting the ontology entities, learning the deep representation of ontology entities, and developing the semantic similarity computation. We take  
Figure 7: Flow Chart of the semantic annotation algorithm. ontologies for alignment as RDF graph formats. These RDF files are processed to remove extra contents and extract the core entities. Learning VO Ontology Representations  
Representation learning provides the opportunity to support semantic interoperability problems through learning representation of ontological concepts, and this has been envisaged through the improved performance of deep learning methods. Moreover, existing approaches do not focus on the higher-level information available in the ontologies. To utilize this opportunity, deep learning-based methods to learn the representation of VO ontological information are utilized. This information is processed through the vector of words which ultimately provide improved performance to the ontology alignment method. The proposed model provides a mechanism to interoperate the domain ontologies by aligning their entities. There exist several types of information with respect to entities in an ontology which include concept or classes in the ontology graph, entities or the individuals, the associated data properties and the object properties, many different types of other data, such as identifiers of entities and comments, which depend on the ontology design as shown in Figure 8.  
We describe the formal expression of each entity information that is used in our model (Equations (1)-(6)). While we define the formal notations (based on our previous study [51]) for the alignment of source ontology (Onto\(\alpha\)) and target ontology (Onto\(\beta\)) at the individual element level, such that data elements in an ontology are expressed as follows:  
\[\text{Onto}_{\alpha} = \left\{