%0 Conference Paper %1 Newman2008RDFMoleculeStore %A Newman, Andrew %A Li, Yuan-Fang %A Hunter, Jane %B Proceedings of the 4th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS) %D 2008 %K %P 1--16 %T A Scale-Out RDF Molecule Store for Improved Co-Identification, Querying and Inferencing %X Semantic inferencing and querying across large scale RDF triple stores is notoriously slow. Our objective is to expedite this process by employ- ing Google’s MapReduce framework to implement scale-out distributed query- ing 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 granulari- ty between RDF graphs and triples. However, the original RDF molecule defi- nition has inherent limitations that will adversely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and or- dering) to overcome these limitations. We then present implementation details for our MapReduce-based RDF molecule store describing: (a) graph decompo- sition into molecules; (b) SPARQL querying across molecules; and (c) mole- cule merging to retrieve the search results. Finally we evaluate the benefits of our approach in the context of the BioMANTA project – an application that re- quires integration and querying across large-scale protein-protein interaction datasets. The results of performance evaluations based on this case study are presented and discussed.

@inproceedings{Newman2008RDFMoleculeStore, abstract = {Semantic inferencing and querying across large scale RDF triple stores is notoriously slow. Our objective is to expedite this process by employ- ing Google’s MapReduce framework to implement scale-out distributed query- ing 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 granulari- ty between RDF graphs and triples. However, the original RDF molecule defi- nition has inherent limitations that will adversely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and or- dering) to overcome these limitations. We then present implementation details for our MapReduce-based RDF molecule store describing: (a) graph decompo- sition into molecules; (b) SPARQL querying across molecules; and (c) mole- cule merging to retrieve the search results. Finally we evaluate the benefits of our approach in the context of the BioMANTA project – an application that re- quires integration and querying across large-scale protein-protein interaction datasets. The results of performance evaluations based on this case study are presented and discussed.}, added-at = {2011-12-12T19:01:04.000+0100}, author = {Newman, Andrew and Li, Yuan-Fang and Hunter, Jane}, biburl = {https://www.bibsonomy.org/bibtex/29ab15479d363e5b7052320c290c08ae4/gergie}, booktitle = {Proceedings of the 4th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS)}, file = {:Newman2008RDFMoleculeStore.pdf:PDF}, groups = {public}, interhash = {ad5a214791408a55ef52652551e1a3e5}, intrahash = {9ab15479d363e5b7052320c290c08ae4}, keywords = {}, month = {October}, pages = {1--16}, timestamp = {2011-12-12T19:01:04.000+0100}, title = {{A Scale-Out RDF Molecule Store for Improved Co-Identification, Querying and Inferencing}}, username = {gergie}, year = 2008 }