Abstract
Manufacturers migrate their processes to Industry 4.0, which includes new technologies for improving productivity and efficiency of operations. One of the issues is capturing, recreating, and documenting the tacit knowledge of the aging workers. However, there are no systematic procedures to incorporate this knowledge into Enterprise Resource Planning systems and maintain a competitive advantage. This paper describes a solution proposal for a tacit knowledge elicitation process for capturing operational best practices of experienced workers in industrial domains based on a mix of algorithmic techniques and a cooperative game. We use domain ontologies for Industry 4.0 and reasoning techniques to discover and integrate new facts from textual sources into an Operational Knowledge Graph. We describe a concepts formation iterative process in a role game played by human and virtual agents through socialization and externalization for knowledge graph refinement. Ethical and societal concerns are discussed as well.
Highlights
Manufacturers migrate their processes to Industry 4.0 innovative practices, which include the adoption of recent technologies for improving productivity and efficiency of operations through visibility and analytics [1]
Knowledge Graph (KG) construction is undoubtedly new for the tacit knowledge elicitation process that we describe for generating an operational knowledge graph in industrial domains
We have described a solution proposal for capturing operational best practices of experienced workers (a.k.a. the tacit or tribal knowledge) in industrial domains for knowledge transfer
Summary
Manufacturers migrate their processes to Industry 4.0 innovative practices, which include the adoption of recent technologies for improving productivity and efficiency of operations through visibility and analytics [1]. Our contribution follows the same bi-partition, but to the best of our knowledge, this is the first work describing a framework for capturing tacit operational best practices of experienced workers in industrial domains based on a cognitive reasoning system and a role-playing game. The conversion of tacit knowledge into organizational knowledge will be promoted by (i) the application of Concept Maps (CM) mining for concepts visualization [7], (ii) the application of domain ontology on a KG for knowledge representation and automatic knowledge generation [8, 9], and (iii) the application of logical and semantic reasoning to infer new knowledge in a continuous learning process for KG alignment and refinement [10] supervised by SMEs. The resulting KG capitalizes the full corporate business knowledge (implicit and explicit) into enterprise assets and can be the base for a conversational AI application for industrial domains such as maintenance operations, troubleshooting, reparations, on-site training, etc., or can be used as a unified base of expertise in an organization or as a unified enterprise semantic search for intelligent information retrieval.
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