## 2 Background and Related Work  
In large scale healthcare environments, providing interoperability among diverse applications has become a crucial requirement nowadays as the lack of data interoperability has become a significant problem in information sharing, integration, and cross-domain application development [25]. Systems generating diverse types of data provide limitations for integration and sharing to support high-level objectives of unified healthcare services. Healthcare platforms use heterogeneous data models, and involve different protocols to extract and process data, even different data terminologies and formats are used for the data management. As well, due to the lack of agreed standards interoperability provisioning solutions are very limited. It has been envisaged that without an appropriate level of interoperability, the full benefits of integrated healthcare applications cannot be achieved. Data integration and sharing are also hampered when similar models are used to represent data, but the meaning of concepts differ among models; creating a gap to interoperate [26]. Such scenarios require the interoperability of semantics. Semantic interoperability provides a common interpretation of the meaning of contents across different systems. Diverse systems could collaborate and integrate their applications based on shared semantic models, schemas, and common metadata and methods that provide mediation among different collaborating systems [27]. Data interoperability has been investigated from diverse perspectives in the existing literature; some approaches provide the standard semantic ontology models [28; 29] to mitigate the heterogeneity, while others use the semantic alignment [30] approaches to mediate among different models. Also, common data models have been utilized by some organizations to minimize heterogeneity in healthcare applications. Semantic interoperability in healthcare applications has been influenced by the use of approaches where ontologies provide a semantic bridge for different applications. Standard ontologies and vocabularies define the domain concepts and their relationships which are used by heterogeneous systems to understand and interoperate with each other. Several research studies and implementations have been explored in academia and industry. Such as [PERSON] et al. [31], proposed an ontology-based approach for semantic interoperability in distributed healthcare information services. To support interoperability in heterogeneous environments where healthcare monitoring could be realized using smart devices, several semantic ontology models have been introduced, such as Commonwealth Scientific and Industrial Research Organization (CSIRO) ontology [32], which is known as an early effort for the description of sensors and their aspects related to functions, operations, and measurements. Another was Sensor Web for Autonomous Mission Operations (SWAMO) [33], which enabled the cooperation among agents with a web of sensors in intelligent environments. Later on, the most widely used ontology in pervasive environments, the W3C Semantic Sensor Network (SSN) ontology [34] was introduced. The SSN ontology defines the concepts related to sensors, actuators, observations, features, related procedures, and samples. The SSN ontology incorporates the SOSA ontology which defines concepts of Sensor, Observation, Samples, and Actuator. Several applications take benefit from the combination of SOSA and SSN, such as sensing and monitoring applications. To enable semantic interoperability across platforms, oneM2M [35] also provides a base ontology for onem2m-based applications. The purpose of providing such an ontology model is to enable other systems to interoperate with the onem2m platform. Describing the semantics has been achieved well with ontology models. However, due to the difference in human opinions about the understanding of domain knowledge, there exist numerous ontologies that differ from each other while describing the same domain concepts.