In this week’s Colloquium, today 18th of April at 3 PM, Patrick Westphal will present the paper ‘Probabilistic Description Logics under the Distribution Semantics‘ by Riguzzi et. al.
Abstract
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 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. The experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size.
The second talk of the colloquium will be Spark/HDFS Big Data Workbench, which enables developers to easily setup HDFS/Spark cluster and run Spark jobs over it (presented by Ivan Ermilov).
