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

Tracking #: 1833-3046

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Daniel Fernández-Álvarez
Jose Emilio Labra-Gayo
Daniel Gayo-Avello

Responsible editor: 
Oscar Corcho

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The use of collaborative knowledge graphs such as Wikidata or DBpedia has increased in the last years. Organizations, universities and 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, each source covers each one of its topics in different depth. This causes the graph to be a better candidate to be exploited in domains of application related to the most important topics rather than the ones with less available information. In order to discover which are the most addressed topics on each source, we propose ClassRank, an algorithm based on aggregated PageRank scores which measure class importance in RDF graphs. In this paper, we test out approach in Wikidata and discuss the collected results by comparing them with the metrics already proposed by Wikidata project. We have found that our approach is more precise than the baseline in high positions of the rankings and that it is able to capture the importance of classes with few but central instances.
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