ClassRank: a Method to Measure Class Relevance in Knowledge Graphs Applied to Wikidata

Tracking #: 1672-2884

This paper is currently under review
Authors: 
Daniel Fernández-Álvarez
Jose Emilio Labra Gayo
Daniel Gayo-Avello

Responsible editor: 
Oscar Corcho

Submission type: 
Full Paper
Abstract: 
The use of collaborative knowledge graphs such as Wikidata or DBpedia has increased in the last years. Several agents such as organizations, universities or individuals have fed those graphs with their own knowledge, producing massive stores of general-purpose data. There are many approaches using the information contained in those initiatives in order to develop applications or to enrich their own data. Nevertheless, not all the topics available in those sources are covered with the same depth. The heterogeneous distribution of data quality causes that only certain portions of those graphs offer appropriate information to be used by third-party applications. We propose ClassRank, an algorithm based on aggregated PageRank scores which measure class relevance in RDF graphs, allowing to detect the sections with greater potential exploitation. We have successfully tested our proposal on Wikidata.
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