Discovering Implicational Knowledge in Wikidata

Abstract

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.

Attribute Selection using Contranominal Scales

“…The Wikipedia [19] dataset depicts the edit relation between authors and articles while the Wiki44k dataset is a dense part of the Wikidata knowledge graph. The original wiki44k dataset was taken from [14], in this work we conduct our experiments on an adapted version by [13]. Finally, the Students dataset [24] depicts grades of students together with properties such as parental level of education.…”

Orometric Methods in Bounded Metric Data

“…This knowledge graph is a structure that stores knowledge via statements, linking entities via properties to values. A detailed description can be found in [25], while [11] gives an explicit mathematical structure to the Wikidata graph and shows how to use the graph for extracting implicational knowledge from Wikidata subsets. We investigate in the following if prominence and isolation of a given municipality can be used as features to predict university locations in a classification setup.…”

Wikidata on MARS

“…Our work was informed by SQID's embodiment of MARPL-based reasoning, and motivated in part by the desire to expand the expressiveness of MARPL rules, as illustrated by the SQID rule set (particularly limitations related to attribute sets) to provide a more complete reasoning framework, and to accommodate Wikidata constraints. [1] also formalizes a model of Wikidata based on MARS, but with a different objective: the application of "Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data". [1] is thus nicely complementary with [9] and with our work, in that it provides a basis for discovering, rather than hand-authoring, new (e)MARPL rules.…”

“…[1] also formalizes a model of Wikidata based on MARS, but with a different objective: the application of "Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data". [1] is thus nicely complementary with [9] and with our work, in that it provides a basis for discovering, rather than hand-authoring, new (e)MARPL rules.…”