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scikit-learn Pipelines Meet Knowledge Graphs

The Python kgextension Package

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The Semantic Web: ESWC 2021 Satellite Events (ESWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12739))

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Abstract

Python is currently the most used platform for data science and machine learning. At the same time, public knowledge graphs have been identified as a valuable source of background knowledge in many data science tasks. In this paper, we introduce the kgextension package for Python, which allows for using knowledge graph in data science pipelines built in Python. The demo shows how data from public knowledge graphs such as DBpedia and Wikidata can be used in data mining pipelines based on the popular Python package scikit-learn. We demonstrate the package’s utility by showing that the prediction accuracy on a popular Kaggle task can be significantly increased by using background knowledge from DBpedia.

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Notes

  1. 1.

    https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html.

  2. 2.

    https://github.com/RDFLib/rdflib.

  3. 3.

    https://scikit-learn.org/.

  4. 4.

    https://github.com/om-hb/kgextension.

  5. 5.

    Such as http://dbpedia.org/resource/*ENTITY*.

  6. 6.

    https://lookup.dbpedia.org/.

  7. 7.

    https://scikit-learn.org/stable/modules/feature_selection.html.

  8. 8.

    https://github.com/om-hb/kgextension/blob/master/examples/book_genre_prediction.ipynb.

  9. 9.

    https://www.kaggle.com/sootersaalu/amazon-top-50-bestselling-books-2009-2019.

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Author information

Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Tabea-Clara Bucher, Xuehui Jiang, Ole Meyer, Stephan Waitz, Sven Hertling & Heiko Paulheim

Authors
  1. Tabea-Clara Bucher
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  2. Xuehui Jiang
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  3. Ole Meyer
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  4. Stephan Waitz
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  5. Sven Hertling
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  6. Heiko Paulheim
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Corresponding author

Correspondence to Heiko Paulheim .

Editor information

Editors and Affiliations

  1. IMEC, Ghent University, Ghent, Belgium

    Ruben Verborgh

  2. Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium

    Anastasia Dimou

  3. University of Chile, Santiago, Chile

    Aidan Hogan

  4. Università degli Studi di Bari, Bari, Italy

    Claudia d'Amato

  5. Faculty of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Ilaria Tiddi

  6. Corporate Technology, Siemens Aktiengesellschaft, München, Bayern, Germany

    Arne Bröring

  7. University of St. Gallen, St. Gallen, St. Gallen, Switzerland

    Simon Mayer

  8. Ghent University, Ghent, Belgium

    Femke Ongenae

  9. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Riccardo Tommasini

  10. Information Infrastructure, FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Karlsruhe, Germany

    Mehwish Alam

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Bucher, TC., Jiang, X., Meyer, O., Waitz, S., Hertling, S., Paulheim, H. (2021). scikit-learn Pipelines Meet Knowledge Graphs. In: Verborgh, R., et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-80418-3_2

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  • Online ISBN: 978-3-030-80418-3

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