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Semantic Partitioning for RDF Datasets

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Information Search, Integration, and Personlization (ISIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 760))

Abstract

Today we are witnessing an explosion in the size and the amount of the available RDF datasets. As such, conventional single node RDF management systems give their position to clustered ones. However most of the currently available clustered RDF database systems partition data using hash functions and/or vertical and horizontal partition algorithms with a significant impact on the number of nodes required for query answering, increasing the total cost of query evaluation. In this paper we present a novel semantic partitioning approach, exploiting both the structure and the semantics of an RDF Dataset, for producing vertical partitions that significantly reduce the number of nodes that should be visited for query answering. To construct these partitions, first we select the most important nodes in a dataset as centroids, using the notion of relevance. Then we use the notion of dependence to assign each remaining node to the appropriate centroid. We evaluate our approach using three real world datasets and demonstrate the nice properties that the constructed partitions possess showing that they significantly reduce the total number of nodes required for query answering while introducing minimal storage overhead.

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Notes

  1. 1.

    https://www.w3.org/TR/rdf-schema/.

  2. 2.

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

  3. 3.

    http://www.ics.forth.gr/isl/index_main.php?l=e&c=656.

  4. 4.

    http://www.ics.forth.gr/isl/3D-SYSTEK/.

  5. 5.

    http://www.myhealthavatar.eu/.

  6. 6.

    http://www.ics.forth.gr/~kondylak/ISWC2016_Evaluation.zip.

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Acknowledgements

This research is implemented through IKY scholarships programme and co-financed by the European Union and Greek national funds through the action entitled “Reinforcement of Postdoctoral Researchers”, in the framework of the Operational Programme “Human Resources Development Program, Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) 2014−2020.

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Authors and Affiliations

  1. Institute of Computer Science, FORTH, N. Plastira 100, Heraklion, Greece

    Georgia Troullinou, Haridimos Kondylakis & Dimitris Plexousakis

Authors
  1. Georgia Troullinou
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  2. Haridimos Kondylakis
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  3. Dimitris Plexousakis
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Corresponding author

Correspondence to Haridimos Kondylakis .

Editor information

Editors and Affiliations

  1. Saint Lab ETIS, Université de Cergy-Pontoise, Pontoise, France

    Dimitris Kotzinos

  2. Laboratoire ETIS, Université de Cergy Pontoise, Cergy Pontoise, France

    Dominique Laurent

  3. Dept Informatique INSA Lyon, Université de Lyon, Villeurbanne Cedex, France

    Jean-Marc Petit

  4. LRI, University of Paris, Orsay, France

    Nicolas Spyratos

  5. Graduate School of Information Science Knowledge Media Laboratory, Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

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Troullinou, G., Kondylakis, H., Plexousakis, D. (2017). Semantic Partitioning for RDF Datasets. In: Kotzinos, D., Laurent, D., Petit, JM., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personlization. ISIP 2016. Communications in Computer and Information Science, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-68282-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-68282-2_7

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  • Online ISBN: 978-3-319-68282-2

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