On-demand Relational Concept Analysis

TL;DR

This paper introduces an efficient algorithm for local exploration of relational datasets using Relational Concept Analysis, enabling targeted navigation within extended concept lattices by generating concepts and their neighbors directly from relational contexts.

Contribution

It proposes a novel algorithm for computing local neighborhoods in extended concept lattices within Relational Concept Analysis, improving exploratory search efficiency.

Findings

Algorithm effectively generates concepts and neighbors from relational contexts.

Supports scalable, localized exploration in relational datasets.

Demonstrated with a practical example.

Abstract

Formal Concept Analysis and its associated conceptual structures have been 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, for instance 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 extended 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. We illustrate it on an example.