Computing Probabilistic Queries in the Presence of Uncertainty via Probabilistic Automata

The emergence of uncertainty as an inherent aspect of RDF and linked data has spurred a number of works of both theoretical and practical interest These works aim to incorporate such information in a meaningful way in the computation of queries. In this paper, we propose a framework of query evaluation in the presence of uncertainty, based on probabilistic automata, which are simple yet efficient computational models. We showcase this method on relevant examples, where we show how to construct and exploit the convenient properties of such automata to evaluate RDF queries with adjustable cutoff. Finally, we present some directions for further investigation on this particular line of research, taking into account possible generalizations of this work.