Abstract
Lattices are a commonly used structure for the representation and analysis of
relational and ontological knowledge. In particular, the analysis of these
requires a decomposition of a large and high-dimensional lattice into a set of
understandably large parts. With the present work we propose /ordinal motifs/
as analytical units of meaning. We study these ordinal substructures (or
standard scales) through (full) scale-measures of formal contexts from the
field of formal concept analysis. We show that the underlying decision problems
are NP-complete and provide results on how one can incrementally identify
ordinal motifs to save computational effort. Accompanying our theoretical
results, we demonstrate how ordinal motifs can be leveraged to retrieve basic
meaning from a medium sized ordinal data set.
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