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Data heterogeneity is the first and foremost reason why semantic interoperability is needed. Simply put, when the same concepts are modelled differently, in different applications, we need semantic interoperability to exchange information among those applications. For example, one of the major barriers to electronic health information interoperability is the heterogeneity of clinical data sources that operate on the foundation of data standard models that restrict the exchange of data external to its domain (Fernández-Breis et al., 2013).

At the technical level, this heterogeneity can be found in three levels (B. Andersen et al., 2014). One is the object level, where information objects cannot clearly be identified as instances of real-world entity classes. Another is the attribute level, where we can find ambiguous identifiers for attributes, poorly- defined data types, and implicit semantics in attribute hierarchies. And the last is the value level, where there are diverse formatting and units of measure and non-standardized coding tables are used instead of controlled vocabularies. For example, the systems in a health network usually employ different standards and terminologies (Marco-Ruiz et al., 2016; Moreno-Conde et al., 2015).

Then, we have the complexity inherent to the information models used, since modelling real world concepts, which are often complex, implies having complex information models. An example of this are healthcare systems, where the dynamic nature of biomedical sciences and systems creates difficulties in achieving their semantic interoperability and maintenance (Nogueira et al., 2015). For example, Dixon et al. (Dixon et al., 2013) and Wright et al. (Wright et al., 2015) detected major challenges to enable client- service SIOp related to difficulties in understanding the semantics of the CDS service interfaces when sharing CDS services among 4 organizations (Marco-Ruiz et al., 2016).

While implementing semantic interoperability solutions, we also often face challenges related to socio- cultural differences. A good example of this is during the development of agreements stating the precise meaning of the exchanged data. According to (Morris et al., 2004) a lesson learned from CASE tool integration is that a primary barrier to increased interoperability is the difficulty of reaching such agreements (Flahive & Jakobsson, 2008). This can be, in part, due to stakeholders having different backgrounds, heterogeneous expertise, unique knowledge, particular needs and specific practices (Bonacin et al., 2014; Liao et al., 2014).

For example, the approach used by healthcare domain experts to interpret and express healthcare concepts, which can vary based on culture, geographical location and educational background is an additional challenge (de Lusignan et al., 2011). Some typical difficulties of knowledge engineering processes include the multi-disciplinary nature of knowledge involving teams of professionals from various fields and specialties and language and communication problems between experts, due to different nationalities or schools of thought. Such difficulties are found directly related to elements in the human processes of cognition, meaning and communication (Bonacin et al., 2016).

Another challenge is related to the approach taken during the development of information models. If, on the one hand, in the IoT, one of the reasons why semantic interoperability is hard to achieve is because of the typical bottom-up proprietary approach in developing applications (Desai et al., 2015). On the other hand, the top-down approach used to develop standards is also considered a barrier to interoperability in healthcare applications (Luz et al., 2015).

Usually, IoT applications are deployed in a bottom-up (sensors, gateways, service and application) manner from a common provider. These providers control the sensor data and data structures, which help them to create intelligent application on top of it. Due to the proprietary approach employed by these providers, the IoT domain has turned into a domain of vertical silos of various IoT applications with no horizontal connectivity between them. In fact, many vendors do not provide open, interoperable frameworks that enable semantics to be defined and managed for using their IoT devices (including the software and applications that works with their IoT devices) (Strassner & Diab, 2016). Consequently, this lack of interoperability with independent services presently endangers the wide acceptability and adoption of the IoT domain, especially for applications that can benefit from multiple devices (Desai et al., 2015).

Simultaneously, the top-down approach of all healthcare informatics standards has been one of the barriers to wider interoperability of healthcare applications (Luz et al., 2015). The life cycle of HL7v3 has shown some development that highlight the challenges of achieving semantic interoperability in healthcare especially using a top-down modeling approach. According to the HL7v3 specifications, the Reference Information Model (RIM) is the cornerstone of the software semantic interoperability on those systems, and relies on open research issues such as reuse, alignment and mappings of ontologies (Bonacin et al., 2014).

Isolated developments are, in fact, a challenge to semantic interoperability. There is evidence that the isolated adoption of terminologies, classifications or ontologies has not been effective in delivering the desired ability to communicate semantically valid extracts of information between independently developed, distributed applications (Kalra & Blobel, 2007). So, in order to effectively communicate, both structure and semantics must be decidable between the communicating parties (Nogueira et al., 2015). However, unfortunately, the distributed nature of ontology development has led to ontological heterogeneity for the same or overlapping domains. For example, in the research domain, the heterogeneity of data sources creates a barrier to scientists trying to establish connections among multiple domains of information (Bonacin et al., 2016). Another example is that as the need to exchange healthcare data continues to grow, the inability to share and communicate patient data across the systems becomes impossible due to the varying data standardization models that are adopted by the health systems, which can only ensure interoperability within its own operational domain (Sinaci & Laleci Erturkmen, 2013) (Blackman, 2017).

Although the field of ontology matching is improving, some challenges have to be addressed such as: 1) large-scale matching evaluation, 2) efficiency of matching techniques, 3) matching with background knowledge, 4) matcher selection, combination and tuning, 5) user involvement, 6) explanation of matching results, 7) social and collaborative matching and 8) alignment management: infrastructure and support (Pavel Shvaiko & Euzenat, 2013).

Another challenge to semantic interoperability is that, often, the right stakeholders are not involved. A critical problem in defining and representing semantics is that, while there are many ontologies being built, most are developed by domain experts and not by semantic web experts. Hence, semantic web best practices are not followed and so those ontologies cannot be reused. On the one hand, users not trained in linguistics and first order logic will often produce poor ontologies. On the other hand, good ontology developers often lack the deep domain expertise to create useful and pragmatic ontologies. Therefore, both have to be involved in the process (Strassner & Diab, 2016).

The number of standards available is also a challenge. The semantic web proposes the use of knowledge representation languages to understand and organize the information produced and shared through the Web. Nevertheless if, on the one hand, some researchers claim that there is a lack of a de facto standardization of models and languages, besides the existence of multiples proprietary solutions (Bonacin et al., 2014). On the other hand, other researchers claim that many of the required standards already exist. Some being formal, and other industry or proprietary standards. Hence, the main issues are the lack of a common architecture, and the fact that individual standards cover a smaller or larger part of the problem, sometimes overlapping and competing (den Hartog et al., 2015).

The complexity and limitations of existing solutions also create difficulties. Nowadays, implementing semantic interoperability is a real challenge for the enterprises of any size, especially for small, medium and micro enterprises. Although there are already plenty of interoperability formats and standards (Core Components, EDI, ebXML) established, they are rarely used due to their complexity (Seleng et al., 2015). In the healthcare domain, for example, standards are reportedly hard to use because of four main reasons (Macia, 2014). First, interoperability standards (HL7, 1987; IHE, 2016) provide limited mechanisms to validate the information to be exchanged (e.g. constraints of clinical concepts). Second, they do not focus on ensuring the same interpretation of the exchanged information from one health information system to another. Third, the adoption of health interoperability standards is not trivial since it requires high effort, technical expertise as well as clinical domain knowledge. Finally, the combined use of standards to achieve semantic interoperability is a field of research (Dentler et al., 2013; Garde et al., 2007; Menárguez-Tortosa & Fernández-Breis, 2011).

Language differences also contribute the the challenging task that is developing semantic interoperability. A language-level difference means that ontologies are written in different formalisms, some of them possibly being more expressive than the others or offering different sets of constructs. Consequently, in

such cases, a normalization process needs to occur. Usually, this means a translation of all formalisms into the one used by the ontology that requires most expressiveness.(Ganzha et al., 2017)

Then there are few reports on successful implementations. The uptake of controlled vocabularies has not been followed by enough reports on the successful exchange of semantically coherent extracts of information between different applications. The few reports of successful implementation were in extremely controlled situations, which has not led to any significant implementations for situations that are typically found in the reality of the healthcare systems (Lewis et al., 2008). (Nogueira et al., 2015). To date, there are no scientific investigations published in academic journals, on the capability of the FHIR technology to provide semantic interoperability (Luz et al., 2015).

Finally, there are quite some open research topics hampering the development of semantic interoperability, including the reuse, alignment and mappings of ontologies (Bonacin et al., 2014). Beyond the typical data-level conflicts (Liu et al., 2007; Ram & Jinsoo Park, 2004) such as data type, data format, data value, and data scaling conflicts, there are additional kinds of data-level conflicts, such as data aggregation, data value property conflicts, property concept conflicts, and data value concept conflicts (Arch-int & Arch-int, 2013) requiring resolution, which suggests the need for future study (Sonsilphong et al., 2016).

Differences at the ontology level arise when there are competing views on the same domain. The problem of matching ontologies (also known as mapping or alignment) has been extensively studied over the years (Euzenat & Shvaiko, 2013; Otero-Cerdeira et al., 2015) and many approaches to overcome it have been proposed (Heflin & Song, 2016; KALFOGLOU & SCHORLEMMER, 2003; N.F. Noy, 2009; Pavel Shvaiko & Euzenat, 2013).

However, while some provide open data models for syntactic interoperability, none provides an open and extensible semantic model for achieving semantic interoperability. The reason for this is that a data model is defined as a technology-dependent mapping of the contents of an information model into a form that is specific to a data store or repository. Consequently, the protocol, language, and other implementation features, used in a data model, vary in their ability to convey semantics (Strassner & Diab, 2016).

Although numerous tools and methods for combining ontologies have been proposed, none of them works fully automatically. Nonetheless, using the semantic web approach to interoperability still has many advantages. For example, if it is feasible to combine the ontologies of the IoT platforms, it is possible to employ semantic reasoning for the discovery and matching of data and various services offered by them (Ganzha et al., 2017).

Finally, despite the perfection of the Archetype Definition Language (ADL) as a formalism to describe healthcare concepts, its successful implementation outside the academic environment, in real healthcare

information systems that are able to share data between distributed applications developed independently is still a question to be answered (Kashfi & Torgersson, 2009).