## 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
\begin{array}{l}(Con^{\alpha}{}_{1...n})\ \cup\ (el^{\alpha}{}_{1,-n})\ \cup\ (dPpr^{\alpha}{}_{1,-n})\ \cup\ (oPpr^{\alpha}{}_{1,-n})\ \cup\ (mObj^{\alpha}{}_{1,-n})\end{array} \right\} \tag{1}\] \[\text{Con}_{\text{O}^{\alpha}} = \left\{ \begin{array}{l}(Con^{\alpha}{}_{1},\ Con^{\alpha}{}_{2 },\ldots,\ Con^{\alpha}{}_{n}\ \right]\in[Con^{\alpha}{}_{domain-\alpha}]\end{array} \right\}\] (2) \[E{}_{\text{I}}{\_\text{O}}_{\alpha} = \left\{ \begin{array}{l}(el^{\alpha}{}_{1},\ el^{\alpha}{}_{2},\ldots,\ el^{\alpha}{}_{n}\ )\end{array} \right\}\in\left\{ \begin{array}{l}E{}^{\text{I}}{}_{ domain-\alpha}\end{array} \right\}\] (3) \[DProp\_{\text{O}}_{\alpha} = \left\{ \begin{array}{l}(dPpr^{\alpha}{}_{1},\ dPpr^{\alpha}{}_{2},\ldots,\ Prpr^{\alpha}{}_{n}\ )\end{array} \right\}\in\left\{ \begin{array}{l}DPpr^{\alpha}{}_{ domain-\alpha}\end{array} \right\}\] (4) \[OProp\_{\text{O}}_{\alpha} = \left\{ \begin{array}{l}(dPpr^{\alpha}{}_{1},\ dPpr^{\alpha}{}_{2},\ldots,\ Prpr^{\alpha}{}_{n}\ )\end{array} \right\}\in\left\{ \begin{array}{l}DPpr^{\alpha}{}_{ domain-\alpha}\end{array} \right\}\] (5) \[MObj\_{\text{O}}_{\alpha} = \left\{ \begin{array}{l}(mObj^{\alpha}{}_{1},\ mObj^{\alpha}{}_{2},\ldots,\ mObj^{\alpha}{}_{n}\ )\end{array} \right\}\in\left\{ \begin{array}{l}MObj^{\alpha}{}_{ domain-\alpha}\end{array} \right\} \tag{6}\]  
The above expression describes the data elements in an ontology Onto\(\alpha\), as a set of individual elements, where Con\(\_\)O\(\alpha\) is the set of all concepts in an ontology hierarchy related to the specific domain, in this case it is _domain_\(-\alpha\). The \(E{}_{\text{I}}{\_\text{O}}_{\alpha}\) defines all the individuals or elements related to the defined classes in the domain ontology. The expression \(DProp\_\)O\(\alpha\) defines data properties associated with each element of the domain, where \(OProp\_\)O\(\alpha\) is a collection of all object properties defined to  
Figure 8: Ontology graphs data descriptions. Diverse ontology alignment approaches have been used in previous researches. However, recently, the ontology alignment process is moving towards learning-based approaches. Research, such as in [49; 52], have realized that the deep neural network-based approaches prove better in ontology alignment using representation learning. We utilize a similar approach to get the learned representation of ontological entities using a model of Auto Encoder (AuE) [56], which learns the abstract representation on the VO data. The learning model is illustrated in Figure 10, where input VO ontology data (VO\({}_{O}\)) are given as B-B-W vector to the model and in the output VO ontology representation (VO\({}_{O}\)) is obtained, that is same to (VO\({}_{O}\)) based on learning the estimate to function (E(VO\({}_{O}\))). The model constitutes an encoder and decoder parts, similar to previous studies [50; 57]. Also, following the existing practices in standard approaches [50; 58], the encoder is defined as \(E(VO_{O})~{}=~{}P\big{(}W_{k}+B_{\parallel}\big{)}\), and the decoder is defined as \(D^{\prime}(E(VO_{O}))~{}=~{}P^{\prime}\big{(}W^{T}(E(VO_{O}))+B_{\parallel} \big{)}\). Function P is an activation function (same as sigmoid \(P^{\prime}(o)~{}=~{}1/(1+\exp(-VO_{O}))\)), whereas \(B\), and \(W\), are bias and weights used in the model, respectively. To get maximum information, the model tries to reduce the errors \(Q(VO_{O}\), \((D(E(VO_{O}))))~{}=~{}\big{(}\big{\|}VO_{O}-~{}D(E(VO_{O}))\big{\|}\big{)}^{2}\) to redevelop the output [57]. Going through transformation process, the proposed model acquires the representations for each entity in the VO ontology.