Top-N recommendations are applied in various real life domains and keep attracting intense attention from researchers and industry. While accuracy has been the prevailing issue of the recommendation problem for the last decades, other facets of the problem, namely diversity and explainability, have received much less attention. In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity. We propose an effective framework DivKG leveraging knowledge graph embedding and determinantal point processes (DPP). First, we capture different kinds of relations among users, items and additional entities through a knowledge graph structure. Then, we represent both entities and relations through graph embedding, using all kind of historical interaction. We use these representations to construct kernel matrices of DPP in order to make top-N diversified predictions. Our empirical results show substantial improvement over the state-of-the-art regarding accuracy and diversity metrics. This paper is a french translation of Gan et al. (2020).