  The mainstream approach to the development of ontologies is merging
ontologies encoding different information, where one of the major difficulties
is that the heterogeneity motivates the ontology merging but also limits
high-quality merging performance. Thus, the entity type (etype) recognition
task is proposed to deal with such heterogeneity, aiming to infer the class of
entities and etypes by exploiting the information encoded in ontologies. In
this paper, we introduce a property-based approach that allows recognizing
etypes on the basis of the properties used to define them. From an
epistemological point of view, it is in fact properties that characterize
entities and etypes, and this definition is independent of the specific labels
and hierarchical schemas used to define them. The main contribution consists of
a set of property-based metrics for measuring the contextual similarity between
etypes and entities, and a machine learning-based etype recognition algorithm
exploiting the proposed similarity metrics. Compared with the state-of-the-art,
the experimental results show the validity of the similarity metrics and the
superiority of the proposed etype recognition algorithm.
