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On-demand Relational Concept Analysis

Alexandre Bazin 1 Jessie Carbonnel 2 Marianne Huchard 2 Giacomo Kahn 3 Priscilla Keip 4 Amirouche Ouzerdine 2 
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : 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.
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Submitted on : Sunday, April 7, 2019 - 4:59:04 PM
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Alexandre Bazin, Jessie Carbonnel, Marianne Huchard, Giacomo Kahn, Priscilla Keip, et al.. On-demand Relational Concept Analysis. ICFCA 2019 - 15th International Conference on Formal Concept Analysis, Jun 2019, Frankfurt, Germany. pp.155-172, ⟨10.1007/978-3-030-21462-3_11⟩. ⟨lirmm-02092140⟩



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