With the development of the Semantic Web, an increasingly large number of organizations represent their data in RDF format. A single machine cannot efficiently process complex queries on RDF graphs. It becomes necessary to use a distributed cluster to store and process large-scale RDF datasets that are required to be partitioned. In this paper, we propose a semantic-aware partitioning method for RDF graphs. Inspired by the PageRank algorithm, classes in the RDF schema graphs are ranked. A novel partitioning algorithm is proposed, which leverages the semantic information of RDF and reduces crossing edges between different fragments. The extensive experiments on both synthetic and real-world datasets show that our semantic-aware RDF graph partitioning outperforms the state-of-the-art methods by a large margin.
CITATION STYLE
Xu, Q., Wang, X., Wang, J., Yang, Y., & Feng, Z. (2017). Semantic-Aware partitioning on RDF graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10366 LNCS, pp. 149–157). Springer Verlag. https://doi.org/10.1007/978-3-319-63579-8_12
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