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Automatic Textual Explanations of Concept Lattices

arXiv 2304.08093 ↗First posted 17 Apr 2023cs.AI cs.HCcited by 2

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Abstract (arXiv)

Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 17 Apr 2023.

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9 of 27 references.

References · 9

  1. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  2. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  3. 2019How model accuracy and explanation fidelity influence user trustcited by 44
  4. 2021Principles of Explanation in Human-AI Systemscited by 25
  5. 2020On the Lattice of Conceptual Measurementscited by 8
  6. 2020Knowledge Cores in Large Formal Contextscited by 7
  7. 2021Quantifying the Conceptual Error in Dimensionality Reductioncited by 6
  8. 2021Attribute Selection using Contranominal Scalescited by 4
  9. 2023Scaling Dimensioncited by 2

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2 papers.