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DBkWik: extracting and integrating knowledge from thousands of Wikis

  • Sven HertlingEmail author
  • Heiko Paulheim
Regular Paper

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.

Keywords

Knowledge graph creation Information extraction Linked open data Knowledge graph matching 

Notes

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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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany

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