Automatic Textual Explanations of Concept Lattices
This addresses the difficulty in understanding large concept lattices for users in data analysis, though it is incremental as it builds on existing formal concept analysis methods.
The paper tackles the problem of making concept lattices comprehensible to untrained users by automatically generating textual explanations using standard scales, demonstrating on a dataset with 531 formal concepts that can be covered by about 100 standard scales.
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.