Large-scale reasoning on expressive horn ontologies

Efficient large-scale reasoning is a fundamental prerequisite for the development of the Semantic Web. In this scenario, it is convenient to reduce standard reasoning tasks to query evaluation over (deductive) databases. From a theoretical viewpoint much has been done. Conversely, from a practical point of view, only a few reasoning services have been developed, which however typically can only deal with lightweight ontologies. To fill the gap, the paper presents owl2dlv, a novel and modern Datalog system for evaluating SPARQL queries over very large OWL 2 knowledge bases. owl2dlv builds on the well-known ASP system dlv by incorporating novel optimizations sensibly reducing memory consumption and a server-like behavior to support multiple-query scenarios. The high potential of owl2dlv for large-scale reasoning is outlined by the results of an experiment on data-intensive benchmarks, and confirmed by the direct interest of a major international industrial player, which has stimulated and partially supported this work.