  Query answering routinely employs knowledge graphs to assist the user in the
search process. Given a knowledge graph that represents entities and
relationships among them, one aims at complementing the search with intuitive
but effective mechanisms. In particular, we focus on the comparison of two or
more entities and the detection of unexpected, surprising properties, called
notable characteristics. Such characteristics provide intuitive explanations of
the peculiarities of the selected entities with respect to similar entities. We
propose a solid probabilistic approach that first retrieves entity nodes
similar to the query nodes provided by the user, and then exploits
distributional properties to understand whether a certain attribute is
interesting or not. Our preliminary experiments demonstrate the solidity of our
approach and show that we are able to discover notable characteristics that are
indeed interesting and relevant for the user.
