Scalable Distributed Reasoning using MapReduce

Presented at: 8th International Semantic Web Conference (ISWC2009)

by Jacopo Urbani, Spyros Kotoulas, Eyal Oren, Frank van Harmelen


We address the problem of scalable distributed reasoning, proposing a technique for materialising the closure of an RDF graph based on MapReduce. We have implemented our approach on top of Hadoop and deployed it on a compute cluster of up to 64 commodity machines. We show that a naive implementation on top of MapReduce is straightforward but performs badly and we present several non-trivial optimisations. Our algorithm is scalable and allows us to compute the RDFS closure of 865M triples from the Web (producing 30B triples) in less than two hours, faster than any other published approach.

Scalable Distributed Reasoning using MapReduce was presented at this event.

Keywords: Semantic Web

Resource URI on the dog food server:

Explore this resource elsewhere: