Analogical Reasoning in Description Logics

This work presents a framework, founded on multi-relational instancebased learning, for inductive (memory-based) reasoning on knowledge bases expressed in Description Logics. The procedure, which exploits a relational dissimilarity measure based on the notion of Information Content, can be employed both to answer to class-membership queries and to predict assertions, that may not be logically entailed by the knowledge base. These tasks may be the baseline for other inductive methods for ontology construction and evolution. In a preliminary experimentation, we show that the method is sound. Besides it is actually able to induce new knowledge that might be acquired in the knowledge base.