Structured data representing entity descriptions often lacks precise type information. That is, it is not known to which type an entity belongs to, or the type is too general to be useful. In this work, we propose to deal with this novel problem of inferring the type semantics of structured data, called typification. We formulate it as a clustering problem and discuss the features needed to obtain several solutions based on existing clustering solutions. Because schema features perform best, but are not abundantly available, we propose an approach to automatically derive them from data. Optimized for the use of schema features, we present TYPifier, a novel clustering algorithm that in experiments, yields better typification results than the baseline clustering solutions.