International audienceIn recent years, the growing application of Knowledge Graphs to new and diverse domains has created the need to make these resources accessible and understandable by users with increasingly diverse backgrounds. Visualization techniques have been widely employed as means to facilitate the exploration and comprehension of such data sources. Moreover, the emerging use of Knowledge Graph Embeddings as input features of Machine Learning methods has given even more visibility to this kind of representation, but raising the new issue of understandability and interpretability of such embeddings. In this paper, we show how visualization techniques can be used to jointly explore and interpret both Knowledge Graphs and Graph Embe...