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DBkWik: extracting and integrating knowledge from thousands of Wikis

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Abstract

Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia. Furthermore, we discuss the potential use of DBkWik as a benchmark for knowledge graph matching.

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Notes

  1. Pronounced dee-bee-quick.

  2. http://dbkwik.org/.

  3. http://creativecommons.org/licenses/by-sa/3.0/.

  4. https://github.com/dbpedia/extraction-framework.

  5. https://www.mediawiki.org/wiki/MediaWiki.

  6. https://meta.wikimedia.org/wiki/Data_dumps/Dump_format.

  7. In the scope of this work, we restrict ourselves to Wikis created using the MediaWiki software, but this is merely a technical, not a conceptual limitation—as long as a Wiki software is able to create reasonably structured Wikis, e.g., allows to create infoboxes, categories, etc., it could be used as a source for knowledge graph creation.

  8. https://github.com/sven-h/extraction-framework.

  9. http://fandom.wikia.com/.

  10. http://www.alexa.com/topsites/category/Computers/Software/Groupware/Wiki/Wiki_Farms.

  11. Hauptseite is German for main page.

  12. The gold standard is available at https://github.com/sven-h/dbkwik.

  13. The short abstract is generated by extracting the first text paragraph from a Wiki page, whereas the long abstract is all text before the first headline.

  14. We restricted the workers to have a 95% approval rate and a minimum of 100 approved HITs (human intelligence tasks), following the recommendations by [25] and [16], and restricted their location to the USA to attract a large fraction of native speakers. We paid $0.40 for a HIT of finding matching pages for 10 pages in two Wikis. In total, the creation of the gold standard took 10 days. Details on the task design as well as the resulting gold standard are available online at https://github.com/sven-h/dbkwik.

  15. In many cases, this can be explained by the notability criteria of Wikipedia, cf. http://en.wikipedia.org/wiki/Wikipedia:Notability.

  16. Given request “site:darkscape.fandom.com” on Google (approximately 2660 results) and Bing (approximately 42,500 results)—tested on 8/8/2019.

  17. Request: “bear site:darkscape.fandom.com” Google: (1) Penguin Hide and Seek/Spawn locations (2) Spria (3) Joseph and Bing: (1)Bear ribs (2) Grizzly bear cub (3) Grizzly bear.

  18. https://duckduckgo.com.

  19. https://duckduckgo.com/api.

  20. page https://memory-alpha.fandom.com/wiki/Jean-Luc_Picard#External_links contains links to Wikipedia Memory Beta etc.

  21. The numbers are taken from [20] and [47]. Note that WebIsALOD does not distinguish classes and instances; therefore, we cannot count classes.

  22. https://wikiapiary.com/wiki/Statistics.

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Acknowledgements

We would like to thank Alexandra Hofmann, Samresh Perchani, and Jan Portisch, who helped developing the first prototype of DBkWik in the course of a student project.

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

    Sven Hertling & Heiko Paulheim

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Correspondence to Sven Hertling.

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Hertling, S., Paulheim, H. DBkWik: extracting and integrating knowledge from thousands of Wikis. Knowl Inf Syst 62, 2169–2190 (2020). https://doi.org/10.1007/s10115-019-01415-5

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