Scalable nonmonotonic reasoning over RDF data using MapReduce

In this paper, we are presenting a scalable method for nonmonotonic rule-based reasoning over Semantic Web Data, using MapReduce. Our work is motivated by the recent unparalleled explosion of available data coming from the Web, sensor readings, databases, ontologies and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. We present a scalable MapReduce-based method for reasoning using defeasible stratified logics. Our results indicate that our method shows good scalability properties and is able to handle a benchmark dataset of 1 billion triples, bringing it on par with state-of-the-art methods for monotonic logics.