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Querying Interlinked Data by Bridging RDF Molecule Templates

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11310))

Abstract

Linked Data initiatives have encouraged the publication of a large number of RDF datasets created by different data providers independently. These datasets can be accessed using different Web interfaces, e.g., SPARQL endpoint; however, federated query engines are still required in order to provide an integrated view of these datasets. Given the large number of Web accessible RDF datasets, SPARQL federated query engines implement query processing techniques to effectively select the relevant datasets that provide the data required to answer a query. Existing federated query engines usually utilize coarse-grained description methods where datasets are characterized based on their vocabularies or schema, and details about data in the dataset are ignored, e.g., classes, properties, or relations. This lack of source description may lead to the erroneous selection of data sources for a query, and unnecessary retrieval of data and source communication, affecting thus the performance of query processing over the federation. We address the problem of federated SPARQL query processing and devise MULDER, a query engine for federations of RDF data sources. MULDER describes data sources in terms of an abstract description of entities belonging to the same RDF class, dubbed as an RDF molecule template, and utilizes them for source selection, and query decomposition and optimization. We empirically study the performance and continuous efficiency of MULDER on existing benchmarks, and compare with respect to existing federated SPARQL query engines. The experimental results suggest that RDF molecule templates empower MULDER, and allow for selection of RDF data sources that not only reduce execution time, but also increase answer completeness and continuous efficiency of MULDER.

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Notes

  1. 1.

    https://www.w3.org/TR/rdf11-concepts/.

  2. 2.

    https://www.w3.org/TR/2013/REC-sparql11-query-20130321/.

  3. 3.

    https://github.com/SDM-TIB/MulderTLDKS.

  4. 4.

    https://networkx.github.io/.

  5. 5.

    BSBM queries can be found in the Appendix A.

  6. 6.

    The graph visualization was generated using the open source software platform cytoscape – http://www.cytoscape.org/.

  7. 7.

    FedBench queries can be found in http://fedbench.fluidops.net/resource/Queries.

  8. 8.

    A lower number of connected components indicates a stronger connectivity.

  9. 9.

    LSLOD queries can be found in Appendix A.

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Acknowledgements

This work has been partially funded by the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642795 (WDAqua), the EU H2020 programme for the projects BigDataEurope (GA 644564), and iASiS (GA 727658). Mikhail Galkin is supported by a scholarship of German Academic Exchange Service (DAAD).

Author information

Authors and Affiliations

  1. L3S Research Center, University of Hannover, Hannover, Germany

    Kemele M. Endris, Maria-Esther Vidal & Sören Auer

  2. Fraunhofer IAIS, Sankt Augustin, Germany

    Mikhail Galkin, Ioanna Lytra, Mohamed Nadjib Mami, Maria-Esther Vidal & Sören Auer

  3. Leibniz Information Centre For Science and Technology University Library (TIB), Hannover, Germany

    Maria-Esther Vidal & Sören Auer

  4. University of Bonn, Bonn, Germany

    Ioanna Lytra

  5. ITMO University, Saint Petersburg, Russia

    Mikhail Galkin

Authors
  1. Kemele M. Endris
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  2. Mikhail Galkin
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  3. Ioanna Lytra
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  4. Mohamed Nadjib Mami
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  5. Maria-Esther Vidal
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  6. Sören Auer
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Corresponding author

Correspondence to Kemele M. Endris .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Roland Wagner

  3. IUT, University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. University of Milan, Crema, Italy

    Ernesto Damiani

  5. University of Michigan-Dearborn, Dearborn, MI, USA

    William I. Grosky

Appendices

Appendices

A BSBM Queries

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B LSLOD Queries

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Endris, K.M., Galkin, M., Lytra, I., Mami, M.N., Vidal, ME., Auer, S. (2018). Querying Interlinked Data by Bridging RDF Molecule Templates. In: Hameurlain, A., Wagner, R., Benslimane, D., Damiani, E., Grosky, W. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX. Lecture Notes in Computer Science(), vol 11310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58415-6_1

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