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  • Semantic-Aware Partitioning on RDF Graphs

    Author(s)
    Xu, Qiang
    Wang, Xin
    Wang, Junhu
    Yang, Yajun
    Feng, Zhiyong
    Griffith University Author(s)
    Wang, John
    Year published
    2017
    Metadata
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    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 ...
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    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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    Journal Title
    Lecture Notes in Computer Science
    Volume
    10366
    DOI
    https://doi.org/10.1007/978-3-319-63579-8_12
    Subject
    Other information and computing sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/354614
    Collection
    • Journal articles

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