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

Saoud Johanna, Gutierrez Alain, Huchard Marianne, Marnotte Pascal, Silvie Pierre, Martin Pierre. 2021. Explicit versus tacit knowledge in duquenne-guigues basis of implications: Preliminary results. In : Proceedings of the workshop on Analyzing Real Data with Formal Concept Analysis (RealDataFCA’2021). Braud Agnès (ed.), Dolques Xavier (ed.), Missaoui Rokia (ed.). Strasbourg : CNRS, 20-27. Worshop Analyzing Real Data with Formal Concept Analysis (RealDataFCA’2021), Strasbourg, France, 29 Juin 2021/2 Juillet 2021.

http://ceur-ws.org/Vol-3151/

Communication avec actes

Prévisualisation

Version publiée - Anglais
Sous licence

.
598786.pdf
Télécharger (184kB) | Prévisualisation

Version Online first - Anglais
Accès réservé aux personnels Cirad
Utilisation soumise à autorisation de l'auteur ou du Cirad.
2021_Soumission_WS_RealDataFCA.pdf
Télécharger (214kB) | Demander une copie

Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/HTFE8T / Url - éditeur : https://icfca2021.sciencesconf.org/page/realdatafca2021

Résumé : 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.

Mots-clés libres : Formal concept analysis, Implication rules, Knowledge base, One Health, Pesticidal plants

Auteurs et affiliations