On-demand relational concept analysis

Bazin Alexandre, Carbonnel Jessie, Huchard Marianne, Kahn Giacomo, Keip Priscilla, Ouzerdine Amirouche. 2019. On-demand relational concept analysis. In : Formal concept analysis: 15th International Conference, ICFCA 2019 Frankfurt, Germany, June 25–28, 2019 Proceedings. Cristea Diana (ed.), Le Ber Florence (ed.), Sertkaya Baris (ed.). Cham : Springer, 155-172. (Lecture Notes in Artificial Intelligence, 11511) ISBN 978-3-030-21461-6 International Conference on Formal Concept Analysis (ICFCA 2019). 15, Francfort, Allemagne, 25 Juin 2019/28 Juin 2019.

https://doi.org/10.1007/978-3-030-21462-3_11

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Résumé : Formal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool.

Mots-clés Agrovoc : méthode statistique, informatique, analyse de données, recherche de l'information, logiciel

Mots-clés complémentaires : Algorithme

Classification Agris : U10 - Informatique, mathématiques et statistiques
C30 - Documentation et information

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