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Semantic inferencing and querying across large-scale RDF triple stores is notoriously slow. The objective is to expedite this process by employing Google's MapReduce framework to implement scale-out distributed querying and reasoning. This approach requires RDF graphs to be decomposed into smaller units that are distributed across computational nodes. RDF Molecules appear to offer an ideal approach - providing an intermediate level of granularity between RDF graphs and triples. However, the original RDF molecule definition has inherent limitations that will adversely affect performance. This paper proposes a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. The paper then presents some implementation details for the MapReduce-based RDF molecule store.
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