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Text-Based Annotation of Scientific Images Using Wikimedia Categories

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 903))

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Abstract

The reuse of scientific raw data is a key demand of Open Science. In the project NOA we foster reuse of scientific images by collecting and uploading them to Wikimedia Commons. In this paper we present a text-based annotation method that proposes Wikipedia categories for open access images. The assigned categories can be used for image retrieval or to upload images to Wikimedia Commons. The annotation basically consists of two phases: extracting salient keywords and mapping these keywords to categories. The results are evaluated on a small record of open access images that were manually annotated.

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Notes

  1. 1.

    For an example of an extremely long capture see Fig. 5 in http://dx.doi.org/10.1002/ece3.2579. Also some parsing errors resulted in long captions.

  2. 2.

    Our source code will be released together with all developed source codes of the NOA project.

  3. 3.

    The images are available on Wikimedia Commons at the following link: https://commons.wikimedia.org/w/index.php?title=Special:ListFiles/Sohmen&ilshowall=1.

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Acknowledgment

The presented work was developed within the NOA Project - Automatic Harvesting, Indexing and Provision of Open Access Figures from the Fields of Engineering and Technology Using the Infrastructure of Wikimedia Commons and Wikidata - funded by the DFG under grant number 315976924. NOA is a cooperative project of the Hochschule Hannover and the Technische Informationsbibliothek Hannover. We would like to thank the NOA project team.

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Authors and Affiliations

  1. University of Applied Sciences and Arts Hanover, Expo Plaza 12, 30539, Hanover, Germany

    Frieda Josi, Christian Wartena & Jean Charbonnier

Authors
  1. Frieda Josi
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  2. Christian Wartena
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  3. Jean Charbonnier
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Corresponding author

Correspondence to Christian Wartena .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

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Josi, F., Wartena, C., Charbonnier, J. (2018). Text-Based Annotation of Scientific Images Using Wikimedia Categories. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-99133-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

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