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\(\textsf {GQA}_{\textsf {RDF}}\): A Graph-Based Approach Towards Efficient SPARQL Query Answering

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Database Systems for Advanced Applications (DASFAA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12113))

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Abstract

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. In graph-based RDF data management solution, SPARQL queries are translated into subgraph patterns and evaluated over RDF graphs via graph matching. However, answering SPARQL queries requires handing RDF reasoning to model implicit triples in RDF data, which is largely overlooked by existing graph-based solutions. In this paper, we investigate to equip graph-based solution with the important RDF reasoning feature for supporting SPARQL query answering. (1) We propose an on-demand saturation strategy, which only selects an RDF fragment that may be potentially affected by the query. (2) We provide a filtering-and-verification framework to efficiently compute the answers of a given query. The framework groups the equivalent entity vertices in the RDF graph to form semantic abstracted graph as index, and further computes the matches according to the multi-grade pruning supported by the index. (3) In addition, we show that the semantic abstracted graph and the graph saturation can be efficiently updated upon the changes to the data graph, enabling the framework to cope with dynamic RDF graphs. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach.

X. Wang and Q. Zhang—Both authors contributed equally to this work.

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Notes

  1. 1.

    https://www.w3.org/RDF/.

  2. 2.

    https://www.w3.org/TR/sparql11-query/.

  3. 3.

    In this paper, we may use SPARQL BGP query" and RDF query" interchangeably.

  4. 4.

    https://www.w3.org/TR/rdf-sparql-query/#BasicGraphPatterns.

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Acknowledgement

This work is partially supported by National Natural Science Foundation of China under Grant No. 61872446, Natural Science Foundation of Hunan Province under Grant No. 2019JJ20024, National key research and development program under Grant Nos. 2018YFB1800203 and 2018YFE0207600.

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Authors and Affiliations

  1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China

    Xi Wang, Qianzhen Zhang, Deke Guo & Xiang Zhao

  2. Hunan University, Changsha, China

    Jianye Yang

Authors
  1. Xi Wang
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  2. Qianzhen Zhang
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  3. Deke Guo
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  4. Xiang Zhao
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  5. Jianye Yang
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Corresponding authors

Correspondence to Deke Guo or Xiang Zhao .

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Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Wang, X., Zhang, Q., Guo, D., Zhao, X., Yang, J. (2020). \(\textsf {GQA}_{\textsf {RDF}}\): A Graph-Based Approach Towards Efficient SPARQL Query Answering. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-59416-9_33

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  • Online ISBN: 978-3-030-59416-9

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