  A referring expression (RE) is a description that identifies a set of
instances unambiguously. Mining REs from data finds applications in natural
language generation, algorithmic journalism, and data maintenance. Since there
may exist multiple REs for a given set of entities, it is common to focus on
the most intuitive ones, i.e., the most concise and informative. In this paper
we present REMI, a system that can mine intuitive REs on large RDF knowledge
bases. Our experimental evaluation shows that REMI finds REs deemed intuitive
by users. Moreover we show that REMI is several orders of magnitude faster than
an approach based on inductive logic programming.
