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International Conference on Database and Expert Systems Applications

DEXA 2017: Database and Expert Systems Applications pp 447–462Cite as

Mining Cardinalities from Knowledge Bases

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10438))

Abstract

Cardinality is an important structural aspect of data that has not received enough attention in the context of RDF knowledge bases (KBs). Information about cardinalities can be useful for data users and knowledge engineers when writing queries, reusing or engineering KBs. Such cardinalities can be declared using OWL and RDF constraint languages as constraints on the usage of properties over instance data. However, their declaration is optional and consistency with the instance data is not ensured. In this paper, we address the problem of mining cardinality bounds for properties to discover structural characteristics of KBs, and use these bounds to assess completeness. Because KBs are incomplete and error-prone, we apply statistical methods for filtering property usage and for finding accurate and robust patterns. Accuracy of the cardinality patterns is ensured by properly handling equality axioms (owl:sameAs); and robustness by filtering outliers. We report an implementation of our algorithm with two variants using SPARQL 1.1 and Apache Spark, and their evaluation on real-world and synthetic data.

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Notes

  1. 1.

    Henceforth, we use prefixes for namespaces according to http://prefix.cc/.

  2. 2.

    https://www.w3.org/TR/shacl/ (accessed on February 13, 2017).

  3. 3.

    http://dublincore.org/documents/dc-dsp/.

  4. 4.

    OWL allows the expression of cardinalities through the minCardinality, maxCardinality, and cardinality restrictions.

  5. 5.

    http://docs.stardog.com/icv/icv-specification.html.

  6. 6.

    http://spinrdf.org/.

  7. 7.

    http://spark.apache.org/ (version 2.1.0).

  8. 8.

    http://www.cyc.com/platform/opencyc.

  9. 9.

    https://www.cs.ox.ac.uk/isg/tools/UOBMGenerator/.

  10. 10.

    http://www.bl.uk/bibliographic/download.html.

  11. 11.

    http://www.dbis.informatik.uni-goettingen.de/Mondial/.

  12. 12.

    https://datahub.io/dataset/nytimes-linked-open-data.

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Acknowledgements

This work has been supported by TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway, Ireland.

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Authors and Affiliations

  1. Fujitsu Ireland Limited, Dublin, Ireland

    Emir Muñoz

  2. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Emir Muñoz & Matthias Nickles

Authors
  1. Emir Muñoz
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  2. Matthias Nickles
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Corresponding author

Correspondence to Emir Muñoz .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Muñoz, E., Nickles, M. (2017). Mining Cardinalities from Knowledge Bases. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_34

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  • DOI: https://doi.org/10.1007/978-3-319-64468-4_34

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  • Print ISBN: 978-3-319-64467-7

  • Online ISBN: 978-3-319-64468-4

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