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We propose a framework to utilize Knowledge Graph (KG) embedding models for conceptual clustering, i.e., the task of clustering a given set of entities in a KG based on the quality of the resulting descriptions for the clusters. Specifically, prominent regions in the embedding space are detected using Multicut clustering algorithm, and then the queries describing/covering the entities within these regions are obtained by rule learning. Finally, we evaluate these queries using different metrics.dblp:conf/semweb/Gad-ElrabHLTS20 fatcat:3idrwcvctrgrvmf5f3xmwaxrvm