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Complex system tacit knowledge extraction trough a formal method

Abstract : A complex system, integrates multiple sub-systems. (Carney, Fisher, & Place, 2005) Each sub-system contains some form of domain knowledge but the main difference that can be seen between a complex system and a set of simple system is that the knowledge the complex system presents is larger than the sum of the knowledge that each sub-system contains (Billaud, Daclin, & Chapurlat, 2015). In both cases, each sub-system is made of elements that are exploitable together, but two different sub-systems have information that can’t be used in concert. Therefore, the tacit knowledge, the knowledge contained in the interaction between the sub-system, is usually harder to extract than the knowledge contained in the sub-systems (Yahia, Lezoche, Aubry, & Panetto, 2011). In “simple cases”, the sub-system can be presented as a formal context, a cross-table of objects and their attributes, and the links between the diverse sub-systems can be represented as relational context, a cross-table containing the objects of two sub-systems, and modeling if two objects are in relation. In this paper, we aim to present the process to extract knowledge from such a model of complex system. We will show how to apply it to a use case of real data from the neurology domain.
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Submitted on : Thursday, December 28, 2017 - 1:55:08 PM
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Mickael Wajnberg, Mario Lezoche, Blondin Alexandre Massé, Petko Valtchev, Hervé Panetto. Complex system tacit knowledge extraction trough a formal method. INSIGHT - International Council on Systems Engineering (INCOSE), Wiley, 2017, 20 (4), pp.23-26. ⟨10.1002/inst.12176⟩. ⟨hal-01673069⟩



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