# Automatic Textual Explanations of Concept Lattices

arXiv: https://arxiv.org/abs/2304.08093 · HTML: https://mlsanity.com/paper/2304.08093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019)
- [How model accuracy and explanation fidelity influence user trust](https://mlsanity.com/paper/1907.12652/) (2019)
- [Principles of Explanation in Human-AI Systems](https://mlsanity.com/paper/2102.04972/) (2021)
- [On the Lattice of Conceptual Measurements](https://mlsanity.com/paper/2012.05267/) (2020)
- [Knowledge Cores in Large Formal Contexts](https://mlsanity.com/paper/2002.11776/) (2020)
- [Quantifying the Conceptual Error in Dimensionality Reduction](https://mlsanity.com/paper/2106.06815/) (2021)
- [Attribute Selection using Contranominal Scales](https://mlsanity.com/paper/2106.10978/) (2021)
- [Scaling Dimension](https://mlsanity.com/paper/2302.09101/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Geometric Structure of Topic Models](https://mlsanity.com/paper/2403.03607/) (2024)
- [A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis](https://mlsanity.com/paper/2606.08477/) (2026)

## 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.