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Explicit versus tacit knowledge in duquenne-guigues basis of implications: Preliminary results

Saoud J., Gutierrez A., Huchard M., Marnotte P., Silvie P., Martin P.. 2021. Strasbourg : s.n., 6 p.. Worshop Analyzing Real Data with Formal Concept Analysis (RealDataFCA¿2021), 2021-06-29/2021-07-02, Strasbourg (France).

DOI: 10.18167/DVN1/HTFE8T

Formal Concept Analysis (FCA) comes with a range of rel- evant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this paper is to collect and discuss the di_erent pat- terns of implications extracted from a dataset on plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, in particular, those corresponding to plant taxonomy, as it is familiar knowl- edge for the experts. Removing these KEs from the implications makes them tacit. We suggest a post-process for cleaning up the implications before reporting them to the experts.

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