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Context-Aware Knowledge Management as an Enabler for Human-Machine Collective Intelligence

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1608))

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Abstract

The effectiveness of maintaining and accessing decentralized organization’s information and knowledge is crucial for the competitiveness of the company and its ability to adapt to ever-changing business environment. Most of this knowledge is typically scattered across various socio-cyber-physical systems inside the company. The concept of socio-cyber-physical system and the related reseach aims on providing a holistic view on heterogeneous systems, including physical, software and human components and their real-time interactions via multilevel connections. Context-aware knowledge management is becoming de facto one of the essential business strategies to support such systems. Its purpose is to facilitate the transfer and exchange of knowledge in the context of business structures and activities related to cultural norms. This paper discussess emerging trends (including role organization, dynamic motivation mechanisms, and multidimensional ontology) in knowledge management for socio-cyber-physical systems. These trends can contribute to the creation of an innovative IT and HR environment based on the collective intelligence of humans and machines, where information and knowledge is shared among participants and among collectives of participants who can be either humans (collective intelligence as methods used by humans to take collective action to solve problems) or software services (based on artificial intelligence models). The paper discusses examples of trends and experiences of their implementation in a global manufacturing company and also proposes a concept human-machine collective intelligence environment utilizing the discussed technologies to support human-computer collaboration in decision support scenarios.

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References

  1. Smirnov, A., Sandkuhl, K.: Context-oriented knowledge management for decision support in business networks: modern requirements and challenges. In: BIR 2015 Workshops, vol. 1420 (2015)

    Google Scholar 

  2. Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16, 97–166 (2001). https://doi.org/10.1207/S15327051HCI16234_02

    Article  Google Scholar 

  3. Smirnov, A., Shilov, N., Ponomarev, A.: Context-aware knowledge management for socio-cyber-physical systems: new trends towards human-machine collective intelligence. In: Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 5–17. SCITEPRESS - Science and Technology Publications (2020). https://doi.org/10.5220/0010171800050017

  4. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5, 199–220 (1993). https://doi.org/10.1006/knac.1993.1008

    Article  Google Scholar 

  5. Staab, S., Studer, R. (eds.): Handbook on Ontologies. IHIS, Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3

    Book  MATH  Google Scholar 

  6. Lundqvist, M.: Information Demand and Use: Improving Information Flow within Small-scale Business Contexts (2007)

    Google Scholar 

  7. Friedrich, J., Becker, M., Kramer, F., Wirth, M., Schneider, M.: Incentive design and gamification for knowledge management. J. Bus. Res. 106, 341–352 (2020). https://doi.org/10.1016/j.jbusres.2019.02.009

    Article  Google Scholar 

  8. Ferreira, A.T., Araújo, A.M., Fernandes, S., Miguel, I.: Gamification in the workplace: a systematic literature review. In: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (eds.) WorldCIST 2017. AISC, vol. 571, pp. 283–292. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56541-5_29

    Chapter  Google Scholar 

  9. Smirnov, A., Kashevnik, A., Petrov, M., Shilov, N., Schafer, T., Jung, T.: Competence-based language expert network for translation business process management. In: 2019 25th Conference of Open Innovations Association (FRUCT), pp. 279–284. IEEE (2019). https://doi.org/10.23919/FRUCT48121.2019.8981515

  10. Scekic, O., Truong, H.-L., Dustdar, S.: PRINGL – a domain-specific language for incentive management in crowdsourcing. Comput. Netw. 90, 14–33 (2015). https://doi.org/10.1016/j.comnet.2015.05.019

    Article  Google Scholar 

  11. Shilov, N., Smirnov, A., Ansari, F.: Ontologies in smart manufacturing: approaches and research framework. In: 2020 26th Conference of Open Innovations Association (FRUCT), pp. 408–414. IEEE (2020). https://doi.org/10.23919/FRUCT48808.2020.9087396

  12. Asmae, A., Souhail, S., Moukhtar, Z. El, Hussein, B.: Using ontologies for the integration of information systems dedicated to product (CFAO, PLM…) and those of systems monitoring (ERP, MES‥). In: 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), pp. 59–64. IEEE (2017). https://doi.org/10.1109/LOGISTIQUA.2017.7962874

  13. Palmer, C., Urwin, E.N., Young, R.I.M., Marilungo, E.: A reference ontology approach to support global product-service production. Int. J. Prod. Lifecycle Manage. 10, 86 (2017). https://doi.org/10.1504/IJPLM.2017.083003

    Article  Google Scholar 

  14. Lafleur, M., Terkaj, W., Belkadi, F., Urgo, M., Bernard, A., Colledani, M.: An onto-based interoperability framework for the connection of PLM and production capability tools. In: Harik, R., Rivest, L., Bernard, A., Eynard, B., Bouras, A. (eds.) PLM 2016. IAICT, vol. 492, pp. 134–145. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-54660-5_13

    Chapter  Google Scholar 

  15. Liao, Y., Lezoche, M., Panetto, H., Boudjlida, N.: Semantic annotations for semantic interoperability in a product lifecycle management context. Int. J. Prod. Res. 54, 5534–5553 (2016). https://doi.org/10.1080/00207543.2016.1165875

    Article  Google Scholar 

  16. Felfernig, A., Friedrich, G., Jannach, D., Stumptner, M., Zanker, M.: Configuration knowledge representations for Semantic Web applications. Artif. Intell. Eng. Des. Anal. Manuf. 17, 31–50 (2003). https://doi.org/10.1017/S0890060403171041

    Article  MATH  Google Scholar 

  17. Hagedorn, T.J., Smith, B., Krishnamurty, S., Grosse, I.: Interoperability of disparate engineering domain ontologies using basic formal ontology. J. Eng. Des. 1–30 (2019). https://doi.org/10.1080/09544828.2019.1630805

  18. Hemam, M., Boufaïda, Z.: MVP-OWL: a multi-viewpoints ontology language for the Semantic Web. Int. J. Reason. Based Intell. Syst. 3, 147 (2011). https://doi.org/10.1504/IJRIS.2011.043539

    Article  Google Scholar 

  19. Hemam, M.: An extension of the ontology web language with multi-viewpoints and probabilistic reasoning. Int. J. Adv. Intell. Parad. 10, 1 (2018). https://doi.org/10.1504/IJAIP.2018.10003857

    Article  Google Scholar 

  20. Smirnov, A., Levashova, T., Shilov, N.: Role-driven context-based decision support: approach, implementation and lessons learned. In: Fred, A., Dietz, J.L.G., Aveiro, D., Liu, K., Filipe, J. (eds.) IC3K 2014. CCIS, vol. 553, pp. 525–540. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25840-9_32

    Chapter  Google Scholar 

  21. VDMA: German Engineering Federation (2018). www.vdma.org/en_GB/

  22. Smirnov, A., Shilov, N., Parfenov, V.: Building a multi-aspect ontology for semantic interoperability in PLM. In: Fortin, C., Rivest, L., Bernard, A., Bouras, A. (eds.) PLM 2019. IAICT, vol. 565, pp. 107–115. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-42250-9_10

    Chapter  Google Scholar 

  23. Fernández-López, M., Gómez-Pérez, A.: Overview and analysis of methodologies for building ontologies. Knowl. Eng. Rev. 17, 129–156 (2002). https://doi.org/10.1017/S0269888902000462

    Article  Google Scholar 

  24. Oroszi, A., Jung, T., Smirnov, A., Shilov, N., Kashevnik, A.: Ontology-driven codification for discrete and modular products. Int. J. Prod. Dev. 8, 162–177 (2009). https://doi.org/10.1504/IJPD.2009.024186

    Article  Google Scholar 

  25. Smirnov, A.V., Shilov, N., Oroszi, A., Sinko, M., Krebs, T.: Changing information management for product-service system engineering: customer-oriented strategies and lessons learned. Int. J. Prod. Lifecycle Manage. 11, 1–18 (2018). https://doi.org/10.1504/IJPLM.2018.091647

    Article  Google Scholar 

  26. Ransbotham, S.: Expanding AI’s impact with organizational learning. Findings from the 2020 Artificial Intelligence Global Executive Study and Research Project (2020)

    Google Scholar 

  27. Jennings, N.R., et al.: Human-agent collectives. Commun. ACM 57, 80–88 (2014). https://doi.org/10.1145/2629559

    Article  Google Scholar 

  28. Retelny, D., Bernstein, M.S., Valentine, M.A.: No workflow can ever be enough: how crowdsourcing workflows constrain complex work. In: Proceedings of the ACM on Human-Computer Interaction, vol. 1, Article 89 (2017). https://doi.org/10.1145/3134724

  29. Karacapilidis, N., Tampakas, V.: On the exploitation of collaborative argumentation structures for inducing reasoning behavior. In: Proceedings of the 18th International Conference on IEEE/Internet (2019)

    Google Scholar 

  30. Ahmad, S., Battle, A., Malkani, Z., Kamvar, S.: The jabberwocky programming environment for structured social computing. In: Proceedings of the 24th annual ACM symposium on User interface software and technology - UIST 2011, pp. 53–64 (2011). https://doi.org/10.1145/2047196.2047203

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Acknowledgements

The research is partially funded by the Russian State Research, project 0073-2019-0005. The research on the human-machine collective intelligence for decision support is funded by the Russian Science Foundation, project 19-11-00126.

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Authors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14th Line 39, St. Petersburg, 199178, Russia

    Alexander Smirnov, Nikolay Shilov & Andrew Ponomarev

Authors
  1. Alexander Smirnov
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  2. Nikolay Shilov
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  3. Andrew Ponomarev
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Corresponding author

Correspondence to Alexander Smirnov .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. University of Madeira, Funchal, Portugal

    David Aveiro

  3. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  4. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  5. Polytechnic Institute of Coimbra – ISEC, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Smirnov, A., Shilov, N., Ponomarev, A. (2022). Context-Aware Knowledge Management as an Enabler for Human-Machine Collective Intelligence. In: Fred, A., Aveiro, D., Dietz, J., Salgado, A., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2020. Communications in Computer and Information Science, vol 1608. Springer, Cham. https://doi.org/10.1007/978-3-031-14602-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-14602-2_5

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  • Online ISBN: 978-3-031-14602-2

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