Discovering Implicational Knowledge in Wikidata

Tom HanikaTom Hanika, Maximilian MarxMaximilian Marx, Gerd StummeGerd Stumme

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Tom Hanika, Maximilian Marx, Gerd Stumme
Discovering Implicational Knowledge in Wikidata
Technical Report, arXiv.org, volume CoRR abs/1902.00916, February 2019

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KurzfassungAbstract
Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling in Wikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wikidata in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.
Weitere Informationen unter:Further Information: Link
Projekt:Project: Wikidata
Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems

@techreport{HMS2019,
  author      = {Tom Hanika and Maximilian Marx and Gerd Stumme},
  title       = {Discovering Implicational Knowledge in Wikidata},
  institution = {arXiv.org},
  year        = {2019},
  month       = {February}
}