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PIWD: A Plugin-Based Framework for Well-Designed SPARQL

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Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

In the real world datasets (e.g., DBpedia query log), queries built on well-designed patterns containing only AND and OPT operators (for short, WDAO-patterns) account for a large proportion among all SPARQL queries. In this paper, we present a plugin-based framework for all SELECT queries built on WDAO-patterns, named PIWD. The framework is based on a parse tree called well-designed AND-OPT tree (for short, WDAO-tree) whose leaves are basic graph patterns (BGP) and inner nodes are the OPT operators. We prove that for any WDAO-pattern, its parse tree can be equivalently transformed into a WDAO-tree. Based on the proposed framework, we can employ any query engine to evaluate BGP for evaluating queries built on WDAO-patterns in a convenient way. Theoretically, we can reduce the query evaluation of WDAO-patterns to subgraph homomorphism as well as BGP since the query evaluation of BGP is equivalent to subgraph homomorphism. Finally, our preliminary experiments on gStore and RDF-3X show that PIWD can answer all queries built on WDAO-patterns effectively and efficiently.

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Notes

  1. 1.

    We give each OPT operator a subscript to differentiate them so that readers understand clearly.

  2. 2.

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

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Acknowledgments

This work is supported by the programs of the National Key Research and Development Program of China (2016YFB1000603), the National Natural Science Foundation of China (NSFC) (61502336), and the open funding project of Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education (K93-9-2016-05). Xiaowang Zhang is supported by Tianjin Thousand Young Talents Program and the project-sponsored by School of Computer Science and Technology in Tianjin University.

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Authors and Affiliations

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

    Xiaowang Zhang, Zhenyu Song & Xin Wang

  2. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Zhiyong Feng

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Xiaowang Zhang, Zhenyu Song, Zhiyong Feng & Xin Wang

  4. Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, 211189, China

    Xiaowang Zhang

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  1. Xiaowang Zhang
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  2. Zhenyu Song
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  3. Zhiyong Feng
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  4. Xin Wang
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Corresponding author

Correspondence to Zhiyong Feng .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Zhang, X., Song, Z., Feng, Z., Wang, X. (2016). PIWD: A Plugin-Based Framework for Well-Designed SPARQL. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_16

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  • Print ISBN: 978-3-319-50111-6

  • Online ISBN: 978-3-319-50112-3

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