Abstract
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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61572353), the Natural Science Foundation of Tianjin (17JCYBJC15400), the Open Fund Project of State Key Lab. for Novel Software Technology (Nanjing University) (KFKT2015B20), and the Australian Research Council (ARC) Discovery Project (DP130103051).
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Xu, Q., Wang, X., Wang, J., Yang, Y., Feng, Z. (2017). Semantic-Aware Partitioning on RDF Graphs. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_12
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DOI: https://doi.org/10.1007/978-3-319-63579-8_12
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