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Managing Human and Artificial Knowledge Bearers

The Creation of a Symbiotic Knowledge Management Approach
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 391)

Abstract

As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.

Keywords

Knowledge management Artificial Intelligence Neuronal systems Design of knowledge-driven systems Symbiotic system design 

References

  1. 1.
    Begler, A., Gavrilova, T.: Artificial intelligence methods for knowledge management systems. Working Papers 15106, Graduate School of Management, St. Petersburg State University (2018)Google Scholar
  2. 2.
    Peffers, K., et al.: The design science research process: a model for producing and presenting information systems research. In: 1st International Conference on Design Science in Information Systems and Technology (DESRIST), vol. 24, pp. 83–106 (2006)Google Scholar
  3. 3.
    Eberhard, J.: Johann August Eberhards synonymisches Handwörterbuch der deutschen Sprache, p. 1802. SchimmelpfennigGoogle Scholar
  4. 4.
    Schlabach, P.: Sitte, Ethik und Moral: eine Begründung. Tredition (2018)Google Scholar
  5. 5.
    Wittgenstein, L.: Philosophische Untersuchungen. Suhrkamp Verlag, Frankfurt am Main (1953)Google Scholar
  6. 6.
    Prechtl, P., Burkard, F.: Metzler Lexikon Philosophie: Begriffe und Definitionen. J.B. Metzler, Stuttgart (2015)Google Scholar
  7. 7.
    Brendel, E.: Wissen. In: Jordan, S., Nimtz, C. (eds.) Lexikon Philosophy - Hundert Grundbegriffe, pp. 308–311. Reclam (2013)Google Scholar
  8. 8.
    Gettier, E.L.: Is justified true belief knowledge? Analysis 23(6), 121–123 (1963)Google Scholar
  9. 9.
    Weinert, F.E.: Vergleichende Leistungsmessung in Schulen - eine umstrittene Selbstverständlichkeit. In: Weinert, F.E. (ed.) Leistungsmessungen in Schulen, pp. 17–32. Beltz, Weinheim (2001)Google Scholar
  10. 10.
    Heyse, V., Erpenbeck, J.: Kompetenztraining: 64 modulare Informations- und Trainingsprogramme für die betriebliche, pädagogische und psychologische Praxis. Schäffer-Poeschel (2009)Google Scholar
  11. 11.
    Schmidt, C.: Arbeitsgedächtnis und fremdsprachliches leseverstehen. Zeitschrift für Fremdsprachenforschung 1(11), 83–101 (2000)Google Scholar
  12. 12.
    Wellenreuther, M.: Forschungsbasierte Schulpdagogik. Schneider Verlag, Hohengehren (2012)Google Scholar
  13. 13.
    Davenport, T.H., Prusak, L.: Working knowledge: how organizations manage what they know. Ubiquity, vol. 2000, August 2000Google Scholar
  14. 14.
    Polanyi, M., Sen, A.: The tacit dimension. University of Chicago Press, reissue edn. (2009)Google Scholar
  15. 15.
    Gronau, N.: Wissen prozessorientiert managen: Methode und Werkzeuge für die Nutzung des Wettbewerbsfaktors Wissen in Unternehmen. Oldenbourg Wissenschaftsverlag (2009)Google Scholar
  16. 16.
    North, K., Brandner, A., Steininger, T.: Die Wissenstreppe: Information – Wissen – Kompetenz, pp. 5–8. Springer, Wiesbaden (2016)Google Scholar
  17. 17.
    Lämmel, U., Cleve, J.: Künstliche Intelligenz. Carl-Hanser Verlag, München (2012)zbMATHGoogle Scholar
  18. 18.
    Solso, R.L.: Kognitive Psychologie. Springer-Lehrbuch. Springer, Heidelberg (2005)Google Scholar
  19. 19.
    Reinmann-Rothmeier, G., Mandl, H.: Wissen. In: Lexikon der Neurowissenschaft. Spektrum Akademischer Verlag, Heidelberg (2000)Google Scholar
  20. 20.
    Girard, J., Girard, J.: Defining knowledge management: toward an applied compendium. Online J. Appl. Knowl. Manag. 3(1), 1–20 (2015)Google Scholar
  21. 21.
    Scheer, A.: ARIS - Modellierungsmethoden, Metamodelle, Anwendungen, 3rd edn. Springer, Heidelberg (1998).  https://doi.org/10.1007/978-3-642-97731-2CrossRefGoogle Scholar
  22. 22.
    Becker, J., Schütte, R., Geib, T., Ibershoff, H.: Grundsätze ordnungsmäßiger modellierung (gom)/westfälische wilhelms–universität münster-institut für wirtschaftsinformatik, ids scheer ag, josef friedr. Bremke & Hoerster GmbH & Co (2000)Google Scholar
  23. 23.
    Remus, U.: Prozessorientiertes wissensmanagement. konzepte und modellierung, Juni 2002Google Scholar
  24. 24.
    Fettke, P., Loos, P.: Referenzmodellierungsforschung. Wirtschaftsinformatik 46, 331–340 (2004)Google Scholar
  25. 25.
    Gronau, N., Müller, C.: Wissensarbeit prozessorientiert modellieren und verbessern (2005)Google Scholar
  26. 26.
    Gronau, N., Müller, C., Korf, R.: KMDL - capturing, analysing and improving knowledge-intensive business processes. J. Univers. Comput. Sci. 11, 452–472 (2005)Google Scholar
  27. 27.
    Hinkelmann, K., Thönssen, B., Probst, F.: Referenzmodellierung für e-government-services. Wirtschaftsinformatik 47(5), 356–366 (2005)Google Scholar
  28. 28.
    Davenport, T., Jarvenpaa, S., Beers, W.: Improving knowledge work processes. Sloan Manag. Rev. 37(4), 53–65 (1996)Google Scholar
  29. 29.
    Allweyer, T.: Modellbasiertes wissensmanagement. Inf. Manag. 13(1), 37–45 (1998)Google Scholar
  30. 30.
    Hinkelmann, K., Karagiannis, D., Telesko, R.: PROMOTE — Methodologie und Werkzeug für geschäftsprozessorientiertes Wissensmanagement, pp. 65–90. Springer, Heidelberg (2002)Google Scholar
  31. 31.
    Heisig, P.: Geschäftsprozessorientiertes Wissensmanagement: effektive Wissensnutzung bei der Planung und Umsetzung von Geschäftsprozessen, ch. GPO-WM: Methode und Werkzeug zum geschäftsprozessorientierten Wissensmanagement, pp. 47–64. Xpert.press. Springer, Heidelberg (2002)Google Scholar
  32. 32.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall Press, Upper Saddle River (2009)zbMATHGoogle Scholar
  33. 33.
    Bostrom, N.: Superintelligence: Paths, Dangers Strategies, 1st edn. Oxford University Press Inc., Oxford (2014)Google Scholar
  34. 34.
    Fettke, P.: Conceptual modelling and artificial intelligence: overview and research challenges from the perspective of predictive business process management. In: Companion Proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers co-located with Modellierung 2020, Vienna, Austria, 19–21 February 2020, pp. 157–164 (2020)Google Scholar
  35. 35.
    Peschl, M.: Cognitive Modelling: Ein Beitrag zur Cognitive Science aus der Perspektive des Konstruktivismus und des Konnektionismus. DUV, Datenverarbeitung, Deutscher Universitätsverlag (1990)Google Scholar
  36. 36.
    Evermann, J., Rehse, J.-R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support. Syst. 100, 129–140 (2017). Smart Business Process ManagementGoogle Scholar
  37. 37.
    Grum, M., Gronau, N.: A visionary way to novel process optimizations – the marriage of the process domain and deep neuronal networks. In: Shishkov, B. (ed.) BMSD 2017. LNBIP, vol. 309, pp. 1–24. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78428-1_1 CrossRefGoogle Scholar
  38. 38.
    Baddeley, A.: Oxford Psychology Series, no. 11. Working Memory, New York, NY, US (1986)Google Scholar
  39. 39.
    Baddeley, A.J.: The episodic buffer: a new component of working memory? Trends Cogn. Sci. 4, 417–423 (2000)Google Scholar
  40. 40.
    Baddeley, A.: Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63(1), 1–29 (2012). PMID: 21961947MathSciNetGoogle Scholar
  41. 41.
    Sweller, J., van Merrienboer, J.J.G., Paas, F.G.W.C.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296 (1998)Google Scholar
  42. 42.
    Clark, R.C., Nguyen, F., Sweller, J., Baddeley, M.: Efficiency in learning: evidence-based guidelines to manage cognitive load. Perform. Improv. 45(9), 46–47 (2006)Google Scholar
  43. 43.
    Lufi, D., Okasha, S., Cohen, A.: Test anxiety and its effect on the personality of students with learning disabilities. Learn. Disabil. Q. 27(3), 176–184 (2004)Google Scholar
  44. 44.
    Heymann, H.: Üben und wiederholen - neu betrachtet. Pädagogik 10, 7–11 (1998)Google Scholar
  45. 45.
    Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 25(1), 107–136 (2001)Google Scholar
  46. 46.
    Weinreich, R., Groher, I.: Software architecture knowledge management approaches and their support for knowledge management activities: a systematic literature review. Inf. Softw. Technol. 80, 265–286 (2016)Google Scholar
  47. 47.
    Tsui, E., Garner, B.J., Staab, S.: The role of artificial intelligence in knowledge management. Knowl. Based Syst. 13(5), 235–239 (2000)Google Scholar
  48. 48.
    Liao, S.: Knowledge management technologies and applications—literature review from 1995 to 2002. Expert Syst. Appl. 25(2), 155–164 (2003)Google Scholar
  49. 49.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)Google Scholar
  50. 50.
    Grum, M., Rapp, S., Gronau, N., Albers, A.: Accelerating knowledge – the speed optimization of knowledge transfers. In: Shishkov, B. (ed.) BMSD 2019. LNBIP, vol. 356, pp. 95–113. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-24854-3_7CrossRefGoogle Scholar
  51. 51.
    Heisig, P.: Integration von Wissensmanagement in Geschäftsprozessen. Dissertation, Technische Universität, Berlin (2005)Google Scholar
  52. 52.
    Mertens, K.: Knowledge Management: Best Practices in Europe. Springer, Heidelberg (2001).  https://doi.org/10.1007/978-3-662-04466-7. http://publica.fraunhofer.de/documents/n-4420.htmlCrossRefGoogle Scholar
  53. 53.
    Bloom, B.S., Engelhart, M.B., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of Educational Objectives. The Classification of Educational Goals. Handbook 1: Cognitive Domain. Longmans Green, New York (1956)Google Scholar
  54. 54.
    Probst, G., Raub, S., Romhardt, K.: Wissen managen - Wie Unternehmen ihre wertvollste Ressource optimal nutzen, 5th edn. Gabler Verlag, Wiesbaden (2006)Google Scholar
  55. 55.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of PotsdamPotsdamGermany

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