# Semantic Web and Knowledge Graphs for Industry 4.0
## 1 Introduction  
The emergence of the Internet of Things (IoT), Internet of Services (IoS), Cyber-Physical Systems (CPS), and closer collaborations between human-machine and machine-machine systems have revolutionized the current industrial landscape resulting in the so-called Industry 4.0 (I4.0) [1]. Technological advancements and the proliferation of different types of field devices such as sensors, embedded systems, and self-governed robots have enhanced I4.0 production. These heterogeneous field devices communicate in real-time and thereby are generating a huge amount of valuable data during the manufacturing process. The generated data can play an important role in several aspects such as enhancing the life cycle of products, on-time and on-demand productions, resource optimizations, product customization, maintenance of machines, and logistic styles [2]. However, the heterogeneous nature of different devices, the variety of their generated data, and their interoperability (or lack thereof) presents challenges for the efficient utilization of I4.0 industrial productions. To tackle such challenges, the Semantic Web including knowledge graphs is one of the possible solutions to obtain and communicate domain knowledge among distributed I4.0 partners [3]. The Semantic Web has revolutionized the existing document-based web into more intelligent systems by integrating data and web content into a more structured web environment whereby software agents can carry out tasks autonomously for users. The semantic web makes use of an ontology to represent the information in a machine-processable structure [4]. Ontologies are the data models that are used to represent the semantics of domain concepts through ontological term, i.e., classes (entities) and relationships (properties). An ontology defines the schema of a domain and does not include any information about a particular individual of a domain. For instance Figure 1 illustrates an ontology with generalized terms, i.e., a class Book is linked to another class Author by a property _hasAuthor_. On the other hand, the insertion of the data instances into ontological terms becomes a Knowledge Graph. An ontology is a subset of a knowledge Graph and is needed for the development of knowledge graph. For example, when a specific instance such as a book named Hour of the Witch is written by an author [PERSON] are mapped into ontological terms of Figure 1, it becomes a knowledge graph as illustrated in Figure 2. [PERSON] et al. reported several definitions of a Knowledge Graph and have made clear the difference between an ontology and a knowledge graph [5]. The emergence of knowledge graphs provides an enterprise-ready data framework analogous to the current status of the Semantic Web by integrating knowledge storage and intelligent discovery. In order to discover additional information from knowledge graphs, graph embedding techniques are used [6]. Even though extensive work has been done in semantic data modelling to facilitate 14.0 applications, due to the complex nature of overall I4.0 systems, the currently available semantic model-based ontologies have several limitations. Three of the major issues are: (1) these production line models do not follow \"Linked Data\" principles and thus are lacking the re-usability of the existing vocabularies, such as _Dublin Core_, _schema.org_; (2) the scope of these models are application-specific (i.e., they cover a limited area such as manufacturing processes, resources, etc.) rather than the overall I4.0 system, ranging from data generation to production; and (3) there is no Industry 4.0-ready knowledge graph that can answers queries due to a lack of real-time data availability [7; 8]. In this paper, we summarise existing approaches for Industry 4.0 and the Semantic Web. Our aim is to highlight major issues and opportunities which could arise from the merger of these existing technologies.