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gTop: An Efficient SPARQL Query Engine

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Web and Big Data (APWeb-WAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13423))

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Abstract

In this demonstration, we present gTop, a top-k query engine based on gStore which supports SPARQL queries over RDF databases. gTop can answer top-k queries with high efficiency and scalability. We use the DP-B algorithm for top-k queries and the DP-any algorithm for any-k queries. We break cyclic queries into pieces of tree queries and use DP-any to solve queries generated before assembling the results to retrieve the origin answers. Experiments show the efficiency of gTop. We provide a demonstration website to show the usage of gTop, where users can query YAGO2 with top-k SPARQL queries.

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References

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  5. Zou, L., Mo, J., Chen, L., Özsu, M.T., Zhao, D.: gStore: answering SPARQL queries via subgraph matching. Proc. VLDB Endow. 4(8), 482–493 (2011). https://doi.org/10.14778/2002974.2002976, http://www.vldb.org/pvldb/vol4/p482-zou.pdf

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Acknowledgements

This work was partially supported by National Key R &D Program of China (2020AAA0105200).

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Authors and Affiliations

  1. Peking University, Beijing, China

    Yuqi Zhou & Lei Zou

  2. Beijing Academy of Artificial Intelligence, Beijing, China

    Gang Cao

Authors
  1. Yuqi Zhou
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  2. Lei Zou
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  3. Gang Cao
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Corresponding author

Correspondence to Yuqi Zhou .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. Newcastle University, Callaghan, NSW, Australia

    Lin Yue

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chuanqi Tao

  4. Jinan University, Guangzhou, China

    Xuming Han

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Diego Calvanese

  6. University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhou, Y., Zou, L., Cao, G. (2023). gTop: An Efficient SPARQL Query Engine. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-25201-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25200-6

  • Online ISBN: 978-3-031-25201-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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