In the semantic web environment, where several independent ontologies are used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived from the semantics of the ontologies that are compared. Rather, mappings are computed by evaluating the similarity of the ontologies terminology and/or of their syntactic structure. In this paper, we propose a new mapping validation approach. The approach is based on the notion of probabilistic mappings and on the use of probabilistic reasoning techniques to enforce a semantic interpretation of similarity mappings as probabilistic and hypothetical relations among ontology elements.

Description Logics (DLs) are gaining more popularity as the foundation of ontology languages for the Semantic Web. As most information in real life is imperfect, there has been an increasing interest recently in extending the expressive power of DLs with uncertainty, for which a number of frameworks have been proposed. In this paper, we introduce an extension of DL which unifies and/or generalizes a number of existing approaches for DLs with uncertainty. For this, we first provide a classification of existing frameworks for DLs with uncertainty on the basis of their underlying certainty formalisms. Using this as a basis, we introduce a generic framework for DL with uncertainty by extending components of the DL framework, i.e., the description language, the knowledge base, and the reasoning services.