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Extracting the Main Path of Historic Events from Wikipedia

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Book cover Network Intelligence Meets User Centered Social Media Networks (ENIC 2017)

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Abstract

The large online encyclopedia “Wikipedia” has become a valuable information resource. However, its large size and the interconnectedness of its pages can make it easy to get lost in detail and difficult to gain a good overview of a topic. As a solution we propose a procedure to extract, summarize, and visualize large categories of historic Wikipedia articles. At the heart of this procedure we apply the method of main path analysis—originally developed for citation networks—to a modified network of linked Wikipedia articles. Beside the aggregation method itself, we describe our data mining process of the Wikipedia datasets and the considerations that guided the visualization of the article networks. Finally, we present our web app that allows to experiment with the procedure on an arbitrary Wikipedia category.

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Notes

  1. 1.

    http://www.ti.inf.uni-due.de/research/tools/wikimainpath/.

  2. 2.

    http://histography.io/.

  3. 3.

    http://wiki.polyfra.me/.

  4. 4.

    https://en.wikipedia.org/wiki/Wiki_markup.

  5. 5.

    https://dumps.wikimedia.org/enwiki/.

  6. 6.

    http://boost-spirit.com/.

  7. 7.

    Around 2 h for extracting the dates in ∼130 GB of Wikipedia pages.

  8. 8.

    https://dumps.wikimedia.org/enwiki/20170501/.

  9. 9.

    Note that in the paper we use a different layout than for the web app.

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Acknowledgements

We would like to thank Ulrich Hoppe and Stephanie Große for interesting and stimulating discussions on the topics of the paper. In addition we would like to thank Issai Zaks for his help with the servers and overall technical support. Finally, we have to thank our anonymous reviewers providing some very valuable feedback and missed references to related work. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under grant GRK 2167, Research Training Group “User-Centred Social Media.”

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Authors and Affiliations

  1. University of Duisburg-Essen, Duisburg, Germany

    Benjamin Cabrera & Barbara König

Authors
  1. Benjamin Cabrera
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  2. Barbara König
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Corresponding author

Correspondence to Benjamin Cabrera .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    H. Ulrich Hoppe

  3. Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Nordrhein-Westfalen, Germany

    Tobias Hecking

  4. Department of Computational Intelligence The Engine Centre, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Piotr Bródka

  5. Faculty of Computer Science and Management The Engine Centre, Wroclaw University of Science and Technology, Wroclaw, Poland

    Przemyslaw Kazienko

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Cabrera, B., König, B. (2018). Extracting the Main Path of Historic Events from Wikipedia. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-90312-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90311-8

  • Online ISBN: 978-3-319-90312-5

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