Large Scale Fuzzy RDF Reasoning Engine

In this thesis, We propose fuzzy pD* semantics which generalizes pD* semanticsto reason over fuzzy RDF data using OWL vocabulary, and further develop the largescale reasoning algorithm. We *rst de*ne the notions of fuzzy RDF graph and fuzzypD* interpretation. We then present a set of fuzzy pD* entailment rules and de*nethe Best Degree Bound (BDB) of a triple derived from a fuzzy RDF graph. We showthe existence of the BDB of an arbitrary triple. We prove soundness and completenessresults for the entailment relation. We also prove that the consistency checking is inP, the entailment is NP-complete, and in P if the target fuzzy RDF graph is ground.Therefore, extending the pD* semantics with fuzzy semantics does not increase thecomputational complexity.The MapReduce framework has proved to be very e*cient for data-intensivetasks. Earlier work has tried to use MapReduce for large scale reasoning for pD*semantics and has shown promising results. We move a step forward to consider s-calable reasoning on top of semantic data under fuzzy pD* semantics. To the best ofourknowledge,thisisthe*rstworktoinvestigatehowMapReducecanhelptosolvethescalability issue of fuzzy OWL reasoning. While most of the optimizations used by theexisting MapReduce framework for pD* semantics are also applicable for fuzzy pD*semantics, unique challenges arise when we handle the fuzzy information. We identifythese key challenges, and propose a solution for tackling each of them. Furthermore,we implement a prototype system for the evaluation purpose. The experimental result-s show that the running time of our system is comparable with that of WebPIE, thestate-of-the-art inference engine for scalable reasoning in pD* semantics.