## 1 Introduction
A view of the healthcare application environments where semantic interoperability provisioning is required is shown in Figure 1. In such a scenario, different stakeholders such as hospitals, clinical rehabilitation centers, government institutes, etc. want to integrate and share data for mutual decision making. A variety of healthcare applications could benefit, if there is a common understanding of the shared data and generic processing mechanisms; which could reduce the cost, enable faster processing, and decrease the overall data management complexity. Figure 1: A view of the data interoperability in heterogeneous healthcare service environments. Data interoperability benefits the global healthcare market, which is increasing with tremendous potential for diverse applications. These application areas include healthcare systems [10], diagnosis and consultation applications [11; 12], telehealth solutions [13], clinical decision support systems [14], health information interchange applications [15], chronic healthcare management applications [16], and several others. Moreover, healthcare services are not only restricted to stationary information services but they have evolved into more dynamic applications, such as remote health monitoring in pervasive and Internet of Things (IoT) enabled environments, such as emotion detection [17; 18], depressive disorder analysis [19], or mental health monitoring [20] from physiological signals collected through smart sensors. However, in these type of applications, data interoperability still pose a major challenge [21]. Applications that provide healthcare services by monitoring the affective human health state, such as emotions, collect data from diverse heterogeneous sources and modalities using wearable and stationary sensors and devices [22], as well as from smartphones [23]. In such applications, heterogeneity in data models, data processing methods, and communication protocols develop interoperability bottleneck and limit the integration of data collected from multiple sources and reduces the flexibility for healthcare information sharing in smart environments. To overcome these challenges, the proposed work contributes to a semantic mediation model and provides a solution for data interoperability in heterogeneous healthcare applications. The model involves several features including the semantic annotation of healthcare data collected from different sources, a semantic alignment mechanism involving the deep representation learning, and the integration of healthcare concepts. The work also contributes a base ontology model to provide the mapping of healthcare concepts from diverse ontologies. It provides a Web of Objects (WoO) [24] based ontology catalog infrastructure to deploy semantic ontologies which could be utilized by diverse healthcare systems to support interoperable data management and processing. The main contributions of this article are as follows:  
* We propose a semantic mediation model to support interoperability provisioning for healthcare data integration, sharing, and exchange;
* A base ontology model has been developed with a standard healthcare vocabularies catalog, supporting the reuse of semantic ontologies across applications;
* We utilize deep representation learning-based mechanisms to mitigate the heterogeneity of semantic data models. * Ontology models have been developed to describe and maintain healthcare knowledge. * The development of interoperable healthcare applications has been simplified by devising Web of objects enabled features with semantic interoperability provisioning capabilities. * We formulated a semantic annotation algorithm and semantic alignment procedures to enhance overall interoperability provisioning. Moreover, the development of data interoperability mechanism based on a common data model has been realized, which enables the harmonization of data from different domains. A reusable catalog of ontologies and standard vocabularies can provide an effective solution when it is supported with Web of Objects enabled features.