Fenoglio, Enzo;
Kazim, Emre;
Latapie, Hugo;
Koshiyama, Adriano;
(2022)
Tacit knowledge elicitation process for industry 4.0.
Discover Artificial Intelligence
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, Article 6. 10.1007/s44163-022-00020-w.
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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.
| Type: | Article |
|---|---|
| Title: | Tacit knowledge elicitation process for industry 4.0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s44163-022-00020-w |
| Publisher version: | https://doi.org/10.1007/s44163-022-00020-w |
| Language: | English |
| Additional information: | © 2022 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Concept maps, Knowledge graph, Ontology, Tacit knowledge, Knowledge management, AI ethics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10161291 |
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