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 (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution 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. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. 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. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.

cplint on SWISH is a web application that allows users to perform reasoning tasks on probabilistic logic programs. Both inference and learning systems can be performed: conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting and particle filtering are used. cplint on SWISH is also able to sample goals' arguments and to graph the results. This paper reports on advances and new features of cplint on SWISH, including the capability of drawing the binary decision diagrams created during the inference processes.

Abstract. Representing uncertain information is very important for modeling real world domains. Recently, the DISPONTE semantics has been proposed for probabilistic description logics. In DISPONTE, the axioms of a knowledge base can be annotated with a set of variables and a real number between 0 and 1. This real number represents the probability of each version of the axiom in which the specified variables are instantiated. In this paper we present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that follow the ALC semantics. BUNDLE 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. The experiments performed by applying BUNDLE to probabilistic knowledge bases show that it can handle ontologies of realistic size and is competitive with the system PRONTO for the probabilistic description logic P-SHIQ(D).

The increasing popularity of the Semantic Web drove to a widespread adoption of Description Logics (DLs) for modeling real world domains. To help the diffusion of DLs, a large number of reasoning algorithms have been developed. Usually these algorithms are implemented in procedural languages such as Java or C++. Most of the reasoners exploit the tableau algorithm which features non-determinism, that is not easily handled by those languages. Prolog directly manages non-determinism, thus is a good candidate for dealing with the tableau’s non-deterministic expansion rules. We present TRILL, for “Tableau Reasoner for descrIption Logics in proLog”, that implements a tableau algorithm and is able to return explanations for queries and their corresponding probability, and TRILLP, for “TRILL powered by Pinpointing formulas”, which is able to compute a Boolean formula representing the set of explanations for a query. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information. We show how TRILL and TRILLP can be used to compute the probability of queries to knowledge bases following DISPONTE semantics. Experiments comparing these with other systems show the feasibility of the approach