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Knowledge graphs (KGs) represent facts in the form of nodes and relationships and are widely used to represent and share knowledge in many different domains. However, their widespread adoption to integrate different data sources and their generation processes have made KGs very complicated and difficult to understand, leading to the advent of new knowledge graph exploration approaches to better understand their contents and extract relevant insights. Nevertheless, the needs of current KGdblp:conf/cidr/LissandriniMHP22 fatcat:442lhikgavdojpnenujmad3jby