  The main objective of explanations is to transmit knowledge to humans. This
work proposes to construct informative explanations for predictions made from
machine learning models. Motivated by the observations from social sciences,
our approach selects data points from the training sample that exhibit special
characteristics crucial for explanation, for instance, ones contrastive to the
classification prediction and ones representative of the models. Subsequently,
semantic concepts are derived from the selected data points through the use of
domain ontologies. These concepts are filtered and ranked to produce
informative explanations that improves human understanding. The main features
of our approach are that (1) knowledge about explanations is captured in the
form of ontological concepts, (2) explanations include contrastive evidences in
addition to normal evidences, and (3) explanations are user relevant.
