Parallel Algorithms for Fuzzy Ontology Reasoning

The need to deal with imprecise and vague information in ontologies is rising in importance, as required by several real-world application domains. As a consequence, there is a growing interest in fuzzy ontologies, which combine ontologies and fuzzy logic theory. In fuzzy ontologies, some reasoning tasks usually become harder to solve, such as the concept subsumption problem and the computation of the Best Degree Bound (BDB) of an axiom. In fact, the current existing algorithms to solve these problems usually require performing some simpler tests several times. In this paper, we present a parallelization of these algorithms, implemented in the DeLorean reasoner, and discuss the encouraging results of an empirical evaluation.