Abstract
Implication is a core notion of Formal Concept Analysis and its extensions. It provides information about the regularities present in the data. When one considers a relational data set of real-size, implications are numerous and their formulation, which combines primitive and relational attributes computed using Relational Concept Analysis framework, is complex. For an expert wishing to answer a question based on such a corpus of implications, having a smart exploration strategy is crucial. In this paper, we propose a visual approach, implemented in a web platform named FCAvizIR, for leveraging such corpus. Comprised of three interactive and coordinated views and a toolbox, FCAvizIR has been designed to explore corpora of implication rules following Schneiderman’s famous mantra “overview first, zoom and filter, then details on demand”. It enables metrics filtering, e.g. fixing a minimum and a maximum support value, and the multiple selection of relations and attributes in the premise and in the conclusion to identify the corresponding subset of implications presented as a list and Euler diagrams. An example of exploration is presented using an excerpt of Knomana to analyze plant-based extracts for controlling pests.
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Notes
- 1.
https://www.knomana.org (accessed: 2024–06).
- 2.
https://fcavizir.lirmm.fr (accessed: 2024–04).
- 3.
https://d3js.org/ (accessed: 2024–03).
- 4.
https://atomiks.github.io/tippyjs/ (accessed: 2024–03).
- 5.
https://gite.lirmm.fr/formal-concept-analysis/fcavizir (accessed: 2024-03).
- 6.
https://www.lirmm.fr/fca4j/ (accessed: 2024-04).
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Acknowledgments
The authors warmly thank Alain Gutierrez for his strong support during this work and for enabling FCA4J to generate output files with the data format as required by FCAvizIR. This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 (#Digitag) and the ANR SmartFCA project, Grant ANR-21-CE23-0023 of the French National Research Agency.
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Musslin, L. et al. (2024). FCAvizIR: Exploring Relational Data Set’s Implications Using Metrics and Topics. In: Cabrera, I.P., Ferré, S., Obiedkov, S. (eds) Conceptual Knowledge Structures. CONCEPTS 2024. Lecture Notes in Computer Science(), vol 14914. Springer, Cham. https://doi.org/10.1007/978-3-031-67868-4_10
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