The increasing availability of structured machine-processable knowledge in the WEB OF DATA calls for machine learning methods to support standard pattern matching and reasoning based services (such as query-answering and inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive inference method for inferring missing properties about individuals: given a class-membership/property value prediction problem, we address the task of identifying relations encoding similarities between individuals, and efficiently propagating knowledge across their relations.