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GraSS: An Efficient Method for RDF Subgraph Matching

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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Abstract

Resource Description Framework (RDF) is a standard data model of the Semantic Web, and it has been widely adopted in various domains in recent years for data and knowledge representation. Unlike queries on relational databases, most of queries applied on RDF data are known as graph queries, expressed in the SPARQL language. Subgraph matching, a basic SPARQL operation, is known to be NP-complete. Coupled with the rapidly increasing volumes of RDF data, it makes efficient graph query processing a very challenging problem. This paper primarily focuses on providing an index scheme and corresponding algorithms that support the efficient solution of such queries. We present a subgraph matching query engine based on the FFD-index which is an indexing mechanism encoding a star subgraph into a bit string. A SPARQL query graph is decomposed into several star query subgraphs which can be efficiently processed benefiting from succinct FFD-index data structure. Extensive evaluation shows that our approach outperforms RDF-3X and gStore on solving subgraph matching.

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Notes

  1. 1.

    http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData.

  2. 2.

    http://swat.cse.lehigh.edu/projects/lubm/.

  3. 3.

    http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/archive/.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (61100049), the National High-tech R&D Program of China (863 Program) (2013AA013204), and the Australia Research Council (ARC) Discovery grants DP130103051.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Xuedong Lyu, Xin Wang & Zhiyong Feng

  2. Faculty of Information Technology, Monash University, Melbourne, Australia

    Yuan-Fang Li

  3. School of Information and Communication Technology, Griffith University, Nathan, Australia

    Junhu Wang

  4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xin Wang

Authors
  1. Xuedong Lyu
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  2. Xin Wang
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  3. Yuan-Fang Li
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  4. Zhiyong Feng
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  5. Junhu Wang
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Corresponding author

Correspondence to Xin Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Lyu, X., Wang, X., Li, YF., Feng, Z., Wang, J. (2015). GraSS: An Efficient Method for RDF Subgraph Matching. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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