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Semantic Integration of Open-Data Tables

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Book cover On the Move to Meaningful Internet Systems: OTM 2016 Conferences (OTM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10033))

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Abstract

With vast amounts of tabular data freely available under several Open-Data initiatives, semantic integration of such datasets is a pressing need. Multiple research efforts have addressed the problem of annotating tabular data. However, to the best of our knowledge, they do not adequately address the problem of semantic integration of tables. A given collection of tables can be semantically integrated along several perspectives or themes. This makes semantic integration a “divergent aggregation” problem. Most existing approaches have focused on interpreting a single table, or rewriting tables to describe an overarching theme that is already provided. In this work, we address semantic integration along two levels: Theme identification (identifying dominant topics or perspectives through which the data can be characterized) and Schematic characterization (classes, relationships and instances that best characterize the data within the theme). The theme need not be represented by a single column, and may span across multiple columns or tables. We use Linked Open data (LOD) cloud to map ontologies that best suit the datasets. Our work also identifies incoherent datasets where a given collection may not have common topics. In such cases we are able to provide guidance on the intersection of semantic footprints of the tables for a judicious selection of the datasets for semantic integration.

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Notes

  1. 1.

    CSV: https://en.wikipedia.org/wiki/Comma-separated_values.

  2. 2.

    The Linking Open Data cloud diagram: http://lod-cloud.net/.

  3. 3.

    RDFConverter: http://www.w3.org/wiki/ConverterToRdf.

  4. 4.

    Virtuoso Sponger: http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/VirtSponger.

  5. 5.

    Linked Data Design Issues: http://www.w3.org/DesignIssues/LinkedData.html.

  6. 6.

    Open Refine: https://github.com/OpenRefine.

  7. 7.

    https://data.gov.in/.

  8. 8.

    https://support.spatialkey.com/spatialkey-sample-csv-data/.

  9. 9.

    https://www.cse.iitb.ac.in/~sunita/wwt/#data.

  10. 10.

    http://www.cs.princeton.edu/courses/archive/spr09/cos435/Notes/relevance_topost.pdf.

  11. 11.

    http://wsl.iiitb.ac.in/index.php/2016/07/17/inferencing-in-the-large-semantic-integration-of-open-data-tables//.

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Acknowledgments

We would like to thank the members of the Web Science Lab for cooperating with the Evaluation as Human Evaluators. The lab members who voluntered as human evaluators are all MS and PhD students pursuing research in the broad areas of Web Science, Semantic Web, Text Mining and Knowledge Representation.

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Authors and Affiliations

  1. Web Science Lab, International Institute of Information Technology, 26/C, Electronics City, Hosur Road, Electronic City, Bengaluru, 560100, Karnataka, India

    Asha Subramanian, Ved Kurien Mathai, Vikkurthi Manikanta, Janaki Vinesh Joshi & Srinath Srinivasa

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  1. Asha Subramanian
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  2. Ved Kurien Mathai
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  3. Vikkurthi Manikanta
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  4. Janaki Vinesh Joshi
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  5. Srinath Srinivasa
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Corresponding author

Correspondence to Asha Subramanian .

Editor information

Editors and Affiliations

  1. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Christophe Debruyne

  2. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. TU Graz, Graz, Austria

    Robert Meersman

  4. La Trobe University, Melbourne, Australia

    Tharam Dillon

  5. Institute of Computer Languages, TU Wien, Vienna, Austria

    eva Kühn

  6. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Declan O'Sullivan

  7. Università degli Studi di Milano Crema, Crema, Italy

    Claudio Agostino Ardagna

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Subramanian, A., Mathai, V.K., Manikanta, V., Joshi, J.V., Srinivasa, S. (2016). Semantic Integration of Open-Data Tables. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_35

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  • DOI: https://doi.org/10.1007/978-3-319-48472-3_35

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-48472-3

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