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Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base \ud(KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability \uddistribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization.\udWe present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm exploits an underlying  DL reasoner, such as Pellet, that is able to return explanations for  queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. \udThe experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size

Probabilistic Description Logics under the distribution semantics