# Interoperability of the Time of Industry 4.0 and the Internet of Things
## 2 Related Work
### A Semantic Approach for Achieving Intelligent Interoperability in Industry 4.0  
Semantic web principles and technologies can serve as semantic interoperability providers. In particular, semantic web technologies such as the Resource Description Framework (RDF), the Resource Description Framework Schema (RDFS), the Simple Protocol and RDF Query Language (SPARQL), the Web Ontology Language (OWL) [6], and a few others have been developed to facilitate shared meaning in the distributed web environment. The development of common conceptualizations, known as ontologies, enable shared meaning of data. OWL [7] facilitates the creation of such ontologies as a W3C standard language. RDF and RDFS can be linked to OWL in order to allow a distributed adoption of these ontologies, such as importing from other online available ontologies. In order to develop sound and effective ontologies, practitioners developed several methodologies; that is, for example, the case of [PERSON] and [PERSON] in [8] or [PERSON] in [9] where they presented a set of best practices that are relevant for Industry 4.0 and IoT, as well. In addition, [PERSON], [PERSON], and [PERSON] in [10] have discussed the IERC-A4C's best practices and recommendations for semantic interoperability with a specific focus on IoT, where they presented semantic web best practices fine-tuned for this particular case. One of the cardinal principles in ontology design consists of reusing existing ontologies. This is not always easy to achieve as it requires a systematic collection and categorization of relevant works. For this purpose, the Linked Open Vocabulary (LOV) ([[http://lov.okfn.org/](http://lov.okfn.org/)]([http://lov.okfn.org/](http://lov.okfn.org/))) is the most well-known catalogue of semantic vocabularies used by academics for sharing their works. It currently lists 22 IoT-related ontologies. However, they are not the only works that are relevant for achieving \"intelligent interoperability\", as [PERSON], [PERSON], [PERSON], and [PERSON] in [11] found over 200 ontology projects related to IoT that have been unlisted in the LOV. The main reasons for this cleaning include: (i) they do not meet the LOV quality requirements; or (ii) they were not leveraging previous works. So as not to lose all these works, [PERSON] created a catalogue independent of LOV and specialized in ontologies for the Internet of Things (LOV4 IOT) ([[http://sensormeasurement.appspot.com/?p=ontologies](http://sensormeasurement.appspot.com/?p=ontologies)]([http://sensormeasurement.appspot.com/?p=ontologies](http://sensormeasurement.appspot.com/?p=ontologies))) that currently lists 382 (as of on 30 December 2018) ontology-based research works. This was not the first time that standardization bodies have tried to harmonize the work from practitioners and academics. In fact, W3C in 2009 did the first major attempt at sorting and selecting existing ontologies relevant to the IoT domain. This initiative fostered the creation of the Semantic Sensor Network (SSN) ontology that was created by the SSN workgroup after the review of 17 existing ontologies and data models. In the same year, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] in [12] did a peer reviewed survey of sensor ontologies developed. Despite the extensive work, practitioners believe that the SSN is not a complete ontology for the IoT domain. Consequently, academics developed many new ontologies to fill the gaps, as reported in [13], the where authors shared a first step towards a more IoT-focused set of ontologies. This is not the only relevant initiative as [PERSON], [PERSON], and [PERSON] in [14] introduced an ontology-based knowledgebase for the design of IoT middleware solutions. In particular, the knowledgebase focuses on heterogeneous devices and device data. [PERSON] et al. [13] surveyed an extensive amount of ontologies in the IoT domain, at both a generic and domain-specific level of generality, as defined by [PERSON], [PERSON], [PERSON], and [PERSON] in [15].