## 1 Introduction  
Innovations in Information and Communication Technologies (ICTs) have revolutionized healthcare services landscape and created several possibilities to collect and manage healthcare data [1] effectively. However, automated healthcare information exchange across services is affected due to the heterogeneity of systems. Consequently, one of the major challenges in healthcare services is of data interoperability, which is spawned when healthcare information services are developed independently in different institutions. They involve diverse data processing methods and eventually materialize into heterogeneous models to maintain healthcare information [2; 3]. Data interoperability has been defined as the capability through which heterogeneous systems and applications could communicate and exchange data and interpret the meaning of data so that it becomes sharable and reusable across systems [2]. It provides huge benefits in terms of integration of multiple systems which enables collaboration and distribution of information. Interoperability can be achieved in different levels; hence, its different types have been considered to realize uniform sharing of information in heterogeneous healthcare environments fully. These include technical interoperability, syntactic interoperability, and semantic interoperability. The technical interoperability is concerned with technological aspects of healthcare platforms and systems, such as those that are associated with specific software elements or that enable communication among different systems. It also encompasses issues regarding protocols and their technical specifications. The syntactical interoperability, on the other hand, defines functions to support syntax level mediation. It is concerned with different data formats, heterogeneous schemas, and diverse interfaces. Besides, the semantic interoperability is concerned with the meaning of data. It defines the true meaning of the contents that are generated by healthcare information services and mutually agreed by different systems that use these contents. Consensus on meaning is required while exchanging the data across systems. The semantic interoperability enables different stakeholders to access and understand data unambiguously. Nowadays, semantic interoperability provisioning in healthcare data applications has become a crucial and the most challenging problem [2; 4; 5; 6]. Several healthcare applications maintain data through isolated technological silos and manage information in different semantic models, where each model is designed specifically to a particular healthcare application. The lack of standards for integration and sharing of healthcare data from diverse systems provide a challenge for developing semantically interoperable cross-domain applications. It is difficult to resolve such heterogeneity through solid database solutions merely because each database is designed by different knowledge engineers with a different set of requirements which results in semantic heterogeneity [7]. Therefore, this article mainly focuses on key aspects of semantic interoperability. The semantic interoperability provisioning requires features such as the common semantic models and semantic mediation mechanisms to maintain a shared understanding of data, eventually providing a mutual exchange, and integration of information. The semantic mediation mechanisms provide intermediation processing and management of data to generalize well for the isolated and heterogeneous data solutions. To develop a semantic interoperability provisioning model, some key procedures have been recognized in previous studies [8; 9]. The semantic interoperability provisioning involves ontological model's core concepts and their metadata in a source domain to be aligned well with the concepts of target domains. Such mediation requires very efficient and reliable mechanisms to achieve effective interoperability for healthcare applications.