# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0
### Ontologies for Industry 4.0 Manufacturing  
Semantic modelling of smart factory, manufacturing production line, and the manufacturing systems interoperability are the crucial feature established in the industry 4.0 production between tangible assets including systems, devices, sensors, etc., connected with other over the internet. In the industry 4.0 context, stress has been given on the alignment of manufacturing systems, processes, resource reconfiguration, etc., in the production line. In the last decade, there have been numerous efforts to represent the domain knowledge of industry 4.0 in form of modular ontologies, i.e., resource, device ontology, process ontology, predictive maintenance ontology, etc., to meet manufacturing production requirements. There have been rigorous efforts to develop ontologies that are aimed to semantically model the manufacturing production line with clear meaning. [PERSON] et al. conducted a survey on the role of knowledge graphs in production and manufacturing [53]. They have reported the bibliometric facts, type of research, statics and application scenarios of the Knowledge Graphs in manufacturing and production. Recently, [PERSON] et al. proposed a SIB framework to integrate Bosch manufacturing data to analyse the surface mounting process pipeline [54]. To experiment with their framework, they have developed surface mounting (SMT) to map the production line data. [PERSON] et al. concatenated domain ontologies on the top of SMT ontology to accomplish the interoperability issue in manufacturing data [55]. Both approaches do not reuse the linked open data reuse principle. [PERSON] et al. proposed a resource configuration-based ontology describing the domain knowledge of the reconfiguration of sensible manufacturing resources using web ontology language (OWL) [56]. The objective of their work is to integrate the CPS equipment through ontology-based resource integration architecture. The data generated are stored as a relational database and is associated and mapped into the model instances of the manufacturing ontology. The proposed ontology for resource reconfiguration is examined using an intelligent manipulator as a use case that verified its manufacturing feasibility. A reconfigurable I4.0 for pharmaceutical products has been proposed to adjust the increasing requirement of flexibility, agility, and low-cost in the health sector [57]. The reconfigurable I4.0 is comprised of three layers, namely executing, deployment, and perception layer. The knowledge graph employed in the perception layer is representing the semantics of manufacturing based on the MASON Ontology responsible for scheduling production plan. In the deployment layer, IEC 61499 standard is implemented for modelling functionality and controlling of machines. The feasibility of the proposed approach is validated by taking a use case of drug packing based on demand. [PERSON] et al. proposed AutomationML ontology to represent the semantic modelling of cyber-physical systems covering data exchange in I4.0 scenario [58]. The semantic-based representation of I4.0 devices in the administration shell provides the integration, identification, data availability, etc., of the devices [59; 60]. Considering the domain of the I4.0 resources, [PERSON] et al. developed a semantic model (ExtruOnt) to describe the knowledge of a manufacturing machine known as an extruder machine that executes extrusion process [61]. Though the scope of the developed ExtruOnt is confined to a specific domain, and provides information about extruder components, three-dimensional representations of components and spatial connections, features, and the sensors capturing data about machine performance. However, it can be used as a reference model to develop ontologies to represent other manufacturing machines in I4.0. Simple as well as combined capabilities of manufacturing resource ontology have been proposed to illustrate the capabilities of the production system [62]. The resources capabilities preliminary conceptual model was produced in [63].