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Discovering Implicational Knowledge in Wikidata

  • Tom Hanika
  • Maximilian MarxEmail author
  • Gerd Stumme
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11511)

Abstract

Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Among the freely available knowledge graphs, Wikidata stands out by being collaboratively edited and curated. Among the vast numbers of facts, complex knowledge is just waiting to be discovered, but the sheer size of Wikidata makes this infeasible for human editors. We apply Formal Concept Analysis to efficiently identify and succinctly represent comprehensible implications that are implicitly present in the data. As a first step, we describe a systematic process to extract conceptual knowledge from Wikidata’s complex data model, thus providing a method for obtaining large real-world data sets for FCA. We conduct experiments that show the principal feasibility of the approach, yet also illuminate some of the limitations, and give examples of interesting knowledge discovered.

Keywords

Wikidata FCA Property dependencies Implications 

Notes

Acknowledgements

This work is partly supported by the German Research Foundation (DFG) in CRC 248 (Perspicuous Systems), CRC 912 (HAEC), and Emmy Noether grant KR 4381/1-1 (DIAMOND).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Knowledge and Data Engineering GroupUniversity of KasselKasselGermany
  2. 2.ITeGUniversity of KasselKasselGermany
  3. 3.Knowledge-Based Systems GroupTU DresdenDresdenGermany

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