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A Model of the Knowledge Assessment Using Bayes’ Network

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Information and Software Technologies (ICIST 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 756))

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Abstract

Modelling of knowledge in production enterprises makes it possible to optimise the processes related to knowledge management. Based on the literature review presented the models using to modeling of knowledge. Following this, the process of acquiring tacit knowledge by the R&D department employees is presented. This article formulates the model of knowledge assessment by means of the Bayesian networks, using the example of a Polish medium manufacturing company from the automotive sector with its own research and development (R&D) department. The authors present the problem of assessing the knowledge acquired from a knowledge form dedicated to this company. The knowledge base has been formulated for which the Bayesian network has been used - the learning algorithm which supports quick identification of the required knowledge and its assessment. Thanks to the implementation of the Bayesian network, it was possible to acquire information on the level of knowledge in a department subject to study; thus it was possible to reduce the time needed for the acquisition of the already gathered resource and its correct identification.

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Authors and Affiliations

  1. University of Zielona Góra, Zielona Góra, Poland

    Małgorzata Śliwa & Justyna Patalas-Maliszewska

Authors
  1. Małgorzata Śliwa
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  2. Justyna Patalas-Maliszewska
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Corresponding author

Correspondence to Justyna Patalas-Maliszewska .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Mikašytė

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Śliwa, M., Patalas-Maliszewska, J. (2017). A Model of the Knowledge Assessment Using Bayes’ Network. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-67642-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67641-8

  • Online ISBN: 978-3-319-67642-5

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