Abstract. The limitations of deductive logic-based approaches at deriving operational knowledge from ontologies may be overcome by inductive (instancebased) methods, which are usually efficient and noise-tolerant. However the evaluation of such methods is made particularly difficult by the open-world semantics which may often cause individuals not to be deductively classified by the reasoner. In this paper an evaluation method is proposed that is suitable for comparing inductive classification methods to standard reasoners. Experimentally we show that the behavior of a nearest neighbor classifier is comparable with the one of a standard reasoner in terms of the proposed indices. 1 Motivation Classification for retrieving resources from a knowledge base (KB) in the context of the Semantic Web (SW) is an important task that is performed by means of logical methods. These may fail due to the inherent incompleteness and incoherence of the KBs caused by their distributed nature. This has gi...