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TAIPAN: Automatic Property Mapping for Tabular Data

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

The Web encompasses a significant amount of knowledge hidden in entity-attributes tables. Bridging the gap between these tables and the Web of Data thus has the potential to facilitate a large number of applications, including the augmentation of knowledge bases from tables, the search for related tables and the completion of tables using knowledge bases. Computing such bridges is impeded by the poor accuracy of automatic property mapping, the lack of approaches for the discovery of subject columns and the mere size of table corpora. We propose Taipan, a novel approach for recovering the semantics of tables. Our approach begins by identifying subject columns using a combination of structural and semantic features. It then maps binary relations inside a table to predicates from a given knowledge base. Therewith, our solution supports both the tasks of table expansion and knowledge base augmentation. We evaluate our approach on a table dataset generated from real RDF data and a manually curated version of the T2D gold standard. Our results suggest that we outperform the state of the art by up to 85 % F-measure.

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Notes

  1. 1.

    http://lodstats.aksw.org.

  2. 2.

    http://lov.okfn.org/.

  3. 3.

    https://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html.

  4. 4.

    https://github.com/aksw/taipan.

  5. 5.

    http://webdatacommons.org/webtables/goldstandard.html.

  6. 6.

    For a complete analysis, see https://github.com/AKSW/TAIPAN-Datasets/tree/master/T2D.

  7. 7.

    https://www.w3.org/Submission/CBD/.

  8. 8.

    https://github.com/aksw/TAIPAN-DBD-Datagen.

  9. 9.

    https://github.com/AKSW/TAIPAN-Synth-Datagen/tree/master/DBpediaTableDataset/tables.

  10. 10.

    Accuracy is defined as a ratio of correctly guessed subject columns to a number of overall guessed subject columns.

  11. 11.

    We contacted the authors to obtain their corpus but were not provided access to it. Still, we followed the specification of the SVM in their paper exactly.

  12. 12.

    We used the classifier implementations from scikit-learn python library at http://scikit-learn.org/. For more information on the implementation, please refer to the Taipan Github repository at https://github.com/AKSW/TAIPAN.

  13. 13.

    http://dws.informatik.uni-mannheim.de/en/research/T2K.

  14. 14.

    https://commoncrawl.org/.

  15. 15.

    http://webdatacommons.org/.

  16. 16.

    http://webdatacommons.org/webtables/.

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Acknowledgments

This work has been supported by Eurostars projects DIESEL (project no. 01QE1512C), the BMWI Project GEISER (project no. 01MD16014) as well as the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227).

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Authors and Affiliations

  1. University of Leipzig, Institute of Computer Science, Leipzig, Germany

    Ivan Ermilov & Axel-Cyrille Ngonga Ngomo

  2. AKSW Research Group, Leipzig, Germany

    Ivan Ermilov & Axel-Cyrille Ngonga Ngomo

Authors
  1. Ivan Ermilov
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Correspondence to Ivan Ermilov .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Ermilov, I., Ngomo, AC.N. (2016). TAIPAN: Automatic Property Mapping for Tabular Data. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_11

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  • Online ISBN: 978-3-319-49004-5

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