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HDT Bitmap Triple Indices for Efficient RDF Data Exploration

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The Semantic Web (ESWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12731))

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Abstract

The exploration of large, unknown RDF data sets is difficult even for users who are familiar with Semantic Web technologies as, e.g., the SPARQL query language. The concept of faceted navigation offers a user-friendly exploration method through filters that are chosen such that no empty result sets occur. Computing such filters is resource intensive, especially for large data sets, and may cause considerable delays in the user interaction. One possibility for improving the performance is the generation of indices for partial solutions. In this paper, we propose and evaluate indices in form of the Bitmap Triple (BT) data structure, generated over the Header-Dictionary-Triples (HDT) RDF compression format. We show that the resulting indices can be utilized to efficiently compute the required exploratory operations for data sets with up to 150 million triples. In the experiments, the BT indices exhibit a stable performance and outperform other deployed approaches in four out of five compared operations.

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Notes

  1. 1.

    https://github.com/rdfhdt/hdt-java.

  2. 2.

    https://github.com/MaximilianWenzel/hdt-bt-indices-java-lib.

  3. 3.

    https://jena.apache.org/documentation/query/index.html.

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Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, University of Ulm, Ulm, Germany

    Maximilian Wenzel & Birte Glimm

  2. derivo GmbH, Ulm, Germany

    Thorsten Liebig

Authors
  1. Maximilian Wenzel
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  2. Thorsten Liebig
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  3. Birte Glimm
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Corresponding authors

Correspondence to Maximilian Wenzel , Thorsten Liebig or Birte Glimm .

Editor information

Editors and Affiliations

  1. Ghent University, Ghent, Belgium

    Ruben Verborgh

  2. Aalborg University, Aalborg, Denmark

    Katja Hose

  3. University of Mannheim, Mannheim, Germany

    Heiko Paulheim

  4. ERCIM, Sophia Antipolis, France

    Pierre-Antoine Champin

  5. University of Siegen, Siegen, Germany

    Maria Maleshkova

  6. Universidad Politécnica de Madrid, Boadilla del Monte, Spain

    Oscar Corcho

  7. eBay Inc., San Jose, CA, USA

    Petar Ristoski

  8. FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen, Germany

    Mehwish Alam

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Wenzel, M., Liebig, T., Glimm, B. (2021). HDT Bitmap Triple Indices for Efficient RDF Data Exploration. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-77385-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77384-7

  • Online ISBN: 978-3-030-77385-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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