Achieving Knowledge-as-a-Service in IIoT-driven smart manufacturing: A crowdsourcing-based continuous enrichment method for Industrial Knowledge Graph

Abstract

The rapid development of the industrial internet of things (IIoT) enlightened more tools and techniques to optimize the smart manufacturing service (SMS) paradigm, emphasizing the services based on pervasive IIoT products. However, the serviceability in the smart manufacturing environment is still limited since most of the conventional services are conducted in a passively reactive mode. The knowledge as a service (KaaS) model is hence to be introduced to emphasize the cognitive capability by actively utilizing massive knowledge, where industrial knowledge graph (IKG) plays as the core to generate context-awareness and proactive services for the optimization of serviceability and productivity. Meanwhile, as building IKGs for specific industrial cases is still plagued by the small scale and the lack of continuous enrichment, ensuring the quality and availability of the IKG are still challenging. Aiming to fill this gap with a practical and systematic approach, this paper proposes a generic crowdsourcing approach for continuously evolving the IKG. Through the IKG continuous enrichment approach, IKG-enabled systems indicate the higher value creation ability to utilize knowledge as a kind of service, rather than just a kind of resource. To further illustrate the proposed approach, a case study of a printed circuit board (PCB) processing machine is given with discussions. As an explorative study, future perspectives are also discussed to attract more open and in-depth studies for more robust applications of KaaS and IKG in the IIoT-driven smart manufacturing environment.