Ontology-Based Access to Probabilistic Data.
Jean Christoph Jung, Carsten Lutz · 2013
Abstract. We propose a framework for querying probabilistic data in the presence of an ontology, arguing that the interplay of probabilities and ontologies is fruitful in applications such as managing data that was extracted from the web. The prime inference problem is computing answer probabilities, and we show that it can be implemented using standard probabilistic database systems, similar to traditional ontologybased data access. We demonstrate that query rewriting into first-order logic is an important tool for our framework. First, it is used to establish a PTime vs. #P dichotomy for the data complexity of this problem by lifting a corresponding result from probabilistic databases. Then, we use it to characterize which pairs of query and TBox are in PTime. Finally, it is shown that non-existence of such a rewriting implies #P-hardness. 1