We are interested in dealing with the heterogeneity of Knowledge bases (KBs), e.g., ontologies and schemas, modeled as sets of entity types (etypes), e.g., person, where each etype is associated with a set of properties, e.g., age or height, via an inheritance hierarchy. A huge literature exists on this topic. A common approach is to model KBs as graphs decorated with labels and reduce the problem of KB matching to that of matching these two elements, \textit{viz.}, labels and structure of the graph. However, labels of etypes are often misplaced, e.g., they are more general or specific than the correct etype, as defined by its properties. Structure-based matching may also lead to wrong conclusions as the properties assigned to an etype in an inheritance hierarchy do not depend on the order by which they are assigned and, therefore, on the specific structure of the graph. In this paper, we propose a novel etype graph matching approach, dealing with the two problems highlighted above, based on two key ideas. The first is to implement matching as a classification task where etypes are characterized by the associated properties. The second is we propose two \textit{property-based} etype similarity metrics, which model the roles that properties have in the definition of an etype. The experimental results show the effectiveness of the algorithm, in particular for those etype graphs with a high number of properties.