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  • 标题:Tacit knowledge elicitation process for industry 4.0
  • 本地全文:下载
  • 作者:Enzo Fenoglio ; Emre Kazim ; Hugo Latapie
  • 期刊名称:Discover Artificial Intelligence
  • 电子版ISSN:2731-0809
  • 出版年度:2022
  • 卷号:2
  • 期号:1
  • 页码:1-15
  • DOI:10.1007/s44163-022-00020-w
  • 语种:English
  • 出版社:Springer
  • 摘要: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.
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