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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2024

RCAviz: Exploratory search in multi-relational datasets represented using relational concept analysis

Résumé

The conceptual structures built with Formal Concept Analysis (FCA) and its extensions are appropriate constructs for supporting Exploratory Search (ES). FCA indeed classifies a set of objects described by Boolean attributes in a concept lattice which is prone to (intra-lattice) navigation. Relational Concept Analysis (RCA), for its part, classifies several sets of objects connected through multiple binary relationships by using logical operators (quantifiers) which can be approximate. The output is a set of interconnected concept lattices, thus adding inter-lattice navigation opportunities. In this paper, we describe the web platform RCAviz, which aims to support such intra- and inter-lattice navigation. The user can select a subset of objects and attributes as a starting point for navigation. Then RCAviz shows the associated concept and its close intra- and inter-lattice neighbors. The user can access to the objects and attributes introduced and inherited in a concept. They then can navigate, i.e. zoom and pan the current view, and move from one concept to another. Additional views show the previous and the next conceptual structures, as well as an history which allows the user to browse its navigation. A navigation example is shown on a real dataset to illustrate the potential of RCAviz for ES.

Dates et versions

hal-04421960 , version 1 (28-01-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Marianne Huchard, Pierre Martin, Emile Muller, Pascal Poncelet, Vincent Raveneau, et al.. RCAviz: Exploratory search in multi-relational datasets represented using relational concept analysis. International Journal of Approximate Reasoning, 2024, 166, pp.109123. ⟨10.1016/j.ijar.2024.109123⟩. ⟨hal-04421960⟩
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