Notable Characteristics Search through Knowledge Graphs

Search engines employ complex data structures to assist the user in the search process. Among these structures, knowledge graphs are vastly used for various search tasks. 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 properties, called notable characteristics. These notable characteristics find large applicability in many domains since they provide non-trivial insights of the entities into consideration in an intuitive and domain-independent fashion. To this end, we propose a novel formulation of the problem of searching and retrieving notable characteristics given an initial set of query nodes. While the traditional comparison of nodes by means of node similarity provides only a score with no explanation, we go one step further. We propose a solid probabilistic approach that first retrieves nodes that are similar to the query nodes provided by the user, and then exploits distributional properties to understand whether a particular attribute is interesting or not. We experimentally evaluate the effectiveness of our approach and show that we are able to discover notable characteristics that are indeed interesting and relevant for the user.