Digital Knowledge Twin: Bridging the Gap Between Physical and Cyber Knowledge Spaces by Generative AI
DOI:
https://doi.org/10.52731/liir.v006.351Keywords:
Digital Knowledge Twin, Gen-Ba Knowledge Management, Generative AIAbstract
The extraction, sharing, and utilization of explicit, latent, and tacit field knowledge (called “Gen-Ba knowledge”) possessed by skilled workers in fields such as healthcare, caregiving, agriculture, maintenance and inspection, and manufacturing, have long been recognized as important yet challenging tasks. Organizations and companies capable of achieving this through human systems hold a competitive advantage. With the advent of generative AI, explicit knowledge that can be codified (documented) has become dramatically more accessible. However, the challenge remains in how to efficiently and effectively handle the latent and tacit Gen-Ba knowledge possessed by humans, which AI cannot directly process. This study proposes a “Digital Knowledge Twin”, which facilitates externalization, combination, and internalization of Gen-Ba knowledge for bridging the gap between physical and cyber spaces. In the externalization phase, Gen-Ba knowledge is accumulated in the physical space as fragments of knowledge, integrating human voice messages, photos, and physical sensor data. Then, these Gen-Ba knowledge fragments are linked with structure of operations in the cyber space in a combination phase. Finally, Gen-Ba knowledge is collaboratively internalized in the physical space through workshops, where internalization means sensemaking and symbol grounding among humans. This paper presents the necessary technologies and system architecture required for the implementation of the “Digital Knowledge Twin.” Gen-Ba knowledge management using generative AI is a challenging issue, and the theoretical contribution of this study is that it provides one promising research direction of future knowledge management.
References
N. Uchihira, T. Nishimura, K. Ijuin, “Human-Centric Digital Twin Focused on “Gen-Ba” Knowledge: Conceptual Model and Examples by Smart Voice Messaging System,” 2023 Portland International Conference on Management of Engineering and Technology (PIC-MET), 2023. https://doi.org/10.23919/PICMET59654.2023.10216875
I. Nonaka and H. Takeuchi, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, 1995.
I. Nonaka and R. Toyama, “The knowledge-creating theory revisited: knowledge creation as a synthesizing process,” Knowledge Management Research & Practice, Vol.1, No.1, pp.2-10, 2023. https://doi.org/10.1057/palgrave.kmrp.8500001
M. Alavi and D.E. Leidner, “Review: knowledge management and knowledge management systems: conceptual foundations and research issues,” MIS quarterly, Vol.25, No.1, 2001, pp.107-136. https://doi.org/10.2307/3250961
M.E. Greiner, T. Böhmann, and H. Krcmar, “A strategy for knowledge management,” Journal of Knowledge Management, Vol. 11, 2007, pp. 3-15. https://doi.org/10.1108/13673270710832127
K. Kreiner, “Tacit knowledge management: the role of artifacts,” Journal of Knowledge Management, Vol.6 No.2, 2002, pp.112-123. https://doi.org/10.1108/13673270210424648
R. Seidlerde Alwis and E. Hartmann, “The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises,” Journal of Knowledge Management, Vol.12, No.1, 2008, pp.133-147. https://doi.org/10.1108/13673270810852449
R. Ribeiro, “Tacit knowledge management,” Phenomenology and the cognitive sciences, Vol.12, No.2, 2013, pp.337-366. https://doi.org/10.1007/s11097-011-9251-x
S. Natek and D. Lesjak, “Knowledge management systems and tacit knowledge,” International Journal of Innovation and Learning, Vo.29, No.2, 2021, pp.166-180. https://doi.org/10.1504/IJIL.2021.112994
X. Hu, et al., “Opportunities and challenges of ChatGPT for design knowledge management,” Procedia CIRP Vol.119, 2023, pp.21-28. https://doi.org/10.1016/j.procir.2023.05.001
M. Liu, et al., “Review of digital twin about concepts, technologies, and industrial applications,” Journal of Manufacturing Systems, Vol.58, Part B, 2021, pp.346-361. https://doi.org/10.1016/j.jmsy.2020.06.017
D. Jones, et al., “Characterising the digital twin: A systematic literature review,” CIRP Journal of Manufacturing Science and Technology, Vol.29, 2020, pp.36-52. https://doi.org/10.1016/j.cirpj.2020.02.002
W. Shengli, “Is human digital twin possible?" Computer Methods and Programs in Biomedicine Update Vol.1, 2021. https://doi.org/10.1016/j.cmpbup.2021.100014
M.E., Miller, E. Spatz, “A unified view of a human digital twin,” Human-Intelligent Systems Integration, 2022, pp.1-11. https://doi.org/10.1007/s42454-022-00041-x
Y. Umeda, et al., “Development of an education program for digital manufacturing system engineers based on ‘Digital Triplet’ concept,” Procedia manufacturing Vol.31, 2019, pp.363-369. https://doi.org/10.1016/j.promfg.2019.03.057
N. Uchihira, et al., “Collaboration management by smart voice messaging for physical and adaptive intelligent services,” Portland International Conference on Management of Engineering and Technology (PICMET2013), 2013.
M. Inoue, K. Toya, R. Ogawa, N. Uchihira, “Fusion of Physical and Human Sensors for Condition Prediction: Preliminary Experiments in Smart Agriculture,” IIAI Letters on Informatics and Interdisciplinary Research, Vol.4, 2023. https://doi.org/10.52731/liir.v004.171
K. Ijuin and T. Nishimura, “Evaluating procedure-based knowledge graph and purpose-based knowledge graph of caregiving experts,” 5th International Conference on ICT Integration in Technical Education (ETLTC2023), 2023. https://doi.org/10.1063/5.0228703
M. Inoue, T. Nishimura, and N. Uchihira, “Human centric digital twin implementation concept utilizing kNeXaR and SVMS,” AIP Conference Proceedings, Vol.2909, No.1, 2023. https://doi.org/10.1063/5.0181873
R. Ogawa, M. Inoue, and N. Uchihira, “Enhancing Knowledge Sharing Workshops with Natural Language Processing in Maintenance Work,” International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC2024), 2024. https://doi.org/10.1109/ITC-CSCC62988.2024.10628182
R. Ogawa, M. Inoue, and N. Uchihira, “The Gen-Ba Knowledge Sharing Workshop Support System Using Large Language Models: Experimental Evaluation in a Plant Cultivation Workshop,” The 7th International Conference on ICT Integration in Technical Education (ETLTC2025), 2025.
N. Uchihira, and M. Yoshida, “Agricultural knowledge management using smart voice messaging systems: Combination of physical and human sensors,” International Conference on Serviceology (ICServ2018), 2018, pp. 148-151.
D. Park, et al., “A Study on Performance Improvement of Prompt Engineering for Genera-tive AI with a Large Language Model,” Journal of Web Engineering, Vol.22, No.8, 2023, pp.1187-1206. https://doi.org/10.13052/jwe1540-9589.2285
D.A. Kolb, Experiential learning: Experience as the source of learning and development, FT press, 2024.
K. Torii, et al., “Improvement of Sharing of Observations and Awareness in Nursing and Caregiving by Voice Tweets,” Serviceology for Designing the Future, Springer, 2016, pp.161-175. https://doi.org/10.1007/978-4-431-55861-3_11
