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Towards Ontology-Driven RDF Analytics

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Advances in Conceptual Modeling (ER 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9382))

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Abstract

The RDF data model lends itself to the organization of graph-structured data. The analysis of such data requires specific tools and techniques broadly summarized as RDF analytics. In particular, traditional approaches to the aggregation of multidimensional data do not apply directly to RDF data due to the lack of information regarding the granularity level of the data and unclear semantics of aggregation. Ontologies, however, may provide the additional information required for RDF data aggregation. Using a vocabulary for ontology-based RDF analytics in conjunction with existing domain ontologies, modelers may declaratively specify aggregated views over RDF data. In this paper we describe the fundamentals of ontology-driven RDF analytics based on RDF, RDF Schema, and SPARQL. We present a proof-of-concept implementation of the basic approach that uses open-source technology, thereby demonstrating feasibility. We further discuss possible future extensions to the basic approach.

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Notes

  1. 1.

    http://www.w3.org/TR/owl2-primer/.

  2. 2.

    http://www.w3.org/TR/turtle/.

  3. 3.

    http://www.w3.org/TR/rdf-schema/#ch_reificationvocab.

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Authors and Affiliations

  1. Johannes Kepler University Linz, Linz, Austria

    Bernd Neumayr, Christoph G. Schuetz & Michael Schrefl

Authors
  1. Bernd Neumayr
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  2. Christoph G. Schuetz
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  3. Michael Schrefl
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Corresponding author

Correspondence to Bernd Neumayr .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Manfred A. Jeusfeld

  2. Indian Institute of Technology, Gujarat, India

    Kamalakar Karlapalem

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Neumayr, B., Schuetz, C.G., Schrefl, M. (2015). Towards Ontology-Driven RDF Analytics. In: Jeusfeld, M., Karlapalem, K. (eds) Advances in Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9382. Springer, Cham. https://doi.org/10.1007/978-3-319-25747-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-25747-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25746-4

  • Online ISBN: 978-3-319-25747-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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