Assertion Prediction with Ontologies through Evidence Combination

Following previous works on inductive methods for ABox reasoning, we propose an alternative method for predicting assertions based on available evidence and the analogical criterion. Once neighbors of a test individual are selected through some distance measures, a combination rule descending from Dempster-Shafer theory can join together the evidence provided by the various neighbor individuals in order to predict unknown values in a learning problem. We show how to exploit the procedure in the problems of determining unknown class- and role-memberships or fillers for datatype properties which may be the basis for many further ABox inductive reasoning algorithms. This work presents also an empirical evaluation of the method on real ontologies.