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Scalable Semantics – The Silver Lining of Cloud Computing

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3 Author(s)
Andrew Newman ; Sch. of lTEE, Univ. of Queensland, Brisbane, QLD ; Yuan-Fang Li ; Jane Hunter

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 some implementation details for our MapReduce-based RDF molecule store. Finally we evaluate the benefits of our approach in the context of the Bio-MANTA project - an application that requires integration and querying across large-scale protein-protein interaction datasets.

Published in:

eScience, 2008. eScience '08. IEEE Fourth International Conference on

Date of Conference:

7-12 Dec. 2008