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Dynamic knowledge mapping guided by data mining: Application on Healthcare

Abstract : The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge.
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Contributor : Daniel Gavrysiak <>
Submitted on : Friday, July 3, 2020 - 2:40:39 PM
Last modification on : Saturday, July 4, 2020 - 3:43:27 AM

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Menaouer Brahami, Baghdad Atmani, Nada Matta. Dynamic knowledge mapping guided by data mining: Application on Healthcare. Journal of Information Processing Systems, 2013, 9 (1), pp.1-30. ⟨10.3745/JIPS.2013.9.1.001⟩. ⟨hal-02888988⟩

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