# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0
### Analysis of Existing Ontological Approaches  
The emergence of the semantic web and the knowledge graphs provides an interface among the various reference architectures to deduce the hidden relationships and the interoperability issues among them [47]. It is believed that the coupling of the semantic web and the knowledge graphs could be able to provide a universal model for accomplishing the overall designing process by aligning all I4.0 reference architecture. In the literature, several refined conceptualizations for the different domains of I4.0 such as sales, devices, processes, and products, etc., are proposed by using either complete or modular-based ontologies as depicted in Table 2. For example, the domain knowledge of the manufacturing production line is conceptualized by utilizing four different modular ontologies for device, product, process, and parameters [7]. On one hand, the modular design could provide the opportunity for extending the domain knowledge; however, the maintenance and complexity requires some efforts. In I4.0, each of the machine units or products at different granularity level such as cell, workstation, and component, etc., are represented through a resource. Furthermore, each of the resources can either be a particular product or service supporting a process that may include a sub process or sub resource, e.g., a transport system on workstation level include sub-process (clamping operation) and resource (griper). The authors in [7; 60] have modelled the manufacturing production line by covering most of the resources but still is lacking some of the main resources such as material, environment temperature, and humidity, etc. Additionally, it is extremely difficult to identify whether the time is related to the production line or product delivery, as it is bounded to the OperatingHours class only. A misconception, that the completed products are considered as to be a part of the manufacturing ontology; whereas it should be a part of the product to better modelled in terms of RAMI4.0. These ontologies also lack the linked data principles that is the re-usability of existing vocabulary. For instance, the property isPartOf links machine with manufacturingfacility, i.e., machine is part of manufacturing facility) which is already defined in the Dublin Core vocabulary but it has not been re-used by the domain ontology [8]. The unavailability of such infrastructure that could be able to support the sharing of data using different ontologies in a universal framework is one of the weaknesses of the existing work. Apart from that the ontological based solutions for I4.0 in the existing work are facing two major challenges: (1) the ontology should follow the standard reference architectures of I4.0, but none of the current ontologies is fully compatible with the reference architectures, and (2) Although, immense data is being generated by I4.0 processes, machines, resources, etc., which is captured in IT systems in different formats and is not interoperable. However, there are no known semantic models that can be applied on the top of the data to cover all the concepts or processes involved in any typical smart manufacturing environment. Given this challenge, we need a semantic model that can be used to build a knowledge graph which in turn provides us with the solutions regarding the industry 4.0. It is believed that the knowledge graphs could provide a baseline for implementing more efficient predictive maintenance and machine learning-based algorithms. In this connection, this study aims to provide insights into building the semantic web and knowledge graphs for enhancing the production line manufacturing of I4.0. The overall schematics of this approach are explained in Figure 5. The resource ontology [8], is split into manufacturing, machine, and product ontology to cover more concepts. In machine ontology, new concepts such as capabilities of machine and power consumption by machines are added. The product ontology is enhanced with concepts used in RAMI 4.0 life cycle dimension.