Towards profiling knowledge graphs
Heiko Paulheim · MADOC (University of Mannheim) · 2017
Knowledge Graphs, such as DBpedia, YAGO, or Wikidata, are valuable resources for building intelligent applications like data analytics tools or recommender systems.Understanding what is in those knowledge graphs is a crucial prerequisite for selecing a Knowledge Graph for a task at hand.Hence, Knowledge Graph profiling -i.e., quantifying the structure and contents of knowledge graphs, as well as their differences -is essential for fully utilizing the power of Knowledge Graphs.In this paper, I will discuss methods for Knowledge Graph profiling, depict crucial differences of the big, well-known Knowledge Graphs, like DBpedia, YAGO, and Wikidata, and throw a glance at current developments of new, complementary Knowledge Graphs such as DBkWik and WebIsALOD.