Abstract
In recent years, due to technological advancements, the concept of Industry 4.0 (I4.0) is gaining popularity, while presenting several technical challenges being tackled by both the industrial and academic research communities. Semantic Web including Knowledge Graphs is a promising technology that can play a significant role in realizing I4.0 implementations. This paper surveys the use of the Semantic Web and Knowledge Graphs for I4.0 from different perspectives such as managing information related to equipment maintenance, resource optimization, and the provision of on-time and on-demand production and services. Moreover, to solve the challenges of limited depth and expressiveness in the current ontologies, we have proposed an enhanced reference generalized ontological model (RGOM) based on Reference Architecture Model for I4.0 (RAMI 4.0). RGOM can facilitate a range of I4.0 concepts including improved asset monitoring, production enhancement, reconfiguration of resources, process optimizations, product orders and deliveries, and the life cycle of products. Our proposed RGOM can be used to generate a knowledge graph capable of providing answers in response to any real-time query.
Highlights
The emergence of the Internet of Things (IoT), Internet of Services (IoS), Cyber-PhysicalSystems (CPS), and closer collaborations between human–machine and machine–machine systems have revolutionized the current industrial landscape resulting in the so-calledIndustry 4.0 (I4.0) [1]
First, we provided a comprehensive review of the available ontological model for building I4.0 knowledge graph that enabled us to find the knowledge gap in terms of open challenges, applications
Once the challenges and applications are identified, they are related through a logical one-to-one mapping mechanism
Summary
The emergence of the Internet of Things (IoT), Internet of Services (IoS), Cyber-PhysicalSystems (CPS), and closer collaborations between human–machine and machine–machine systems have revolutionized the current industrial landscape resulting in the so-calledIndustry 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 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]
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