A scale-out RDF molecule store for improved co-identification, querying and inferencing
Andrew F. Newman, Y-F. Li, Jane Hunter · 2008
Abstract. Semantic inferencing and querying across large scale RDF triple stores is notoriously slow. Our 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. In this paper, we propose a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. We then present implementation details for our MapReduce-based RDF molecule store describing: (a) graph decomposition into molecules; (b) SPARQL querying across molecules; and (c) molecule merging to retrieve the search results. Finally we evaluate the benefits of our approach in the context of the BioMANTA project – an application that requires integration and querying across large-scale protein-protein interaction datasets. The results of performance evaluations based on this case study are presented and discussed.