  In recent research, human-understandable explanations of machine learning
models have received a lot of attention. Often explanations are given in form
of model simplifications or visualizations. However, as shown in cognitive
science as well as in early AI research, concept understanding can also be
improved by the alignment of a given instance for a concept with a similar
counterexample. Contrasting a given instance with a structurally similar
example which does not belong to the concept highlights what characteristics
are necessary for concept membership. Such near misses have been proposed by
Winston (1970) as efficient guidance for learning in relational domains. We
introduce an explanation generation algorithm for relational concepts learned
with Inductive Logic Programming (\textsc{GeNME}). The algorithm identifies
near miss examples from a given set of instances and ranks these examples by
their degree of closeness to a specific positive instance. A modified rule
which covers the near miss but not the original instance is given as an
explanation. We illustrate \textsc{GeNME} with the well known family domain
consisting of kinship relations, the visual relational Winston arches domain
and a real-world domain dealing with file management. We also present a
psychological experiment comparing human preferences of rule-based,
example-based, and near miss explanations in the family and the arches domains.
