Knowledge graphs (KGs) have been widely used in recent years and proved to be useful in many applications. However, large scale KGs that are collected by crowdsourcing or constructed by automated algorithms, are usually incomplete. Most existing works focus on how to complete the KG based on embedding learning, but ignore the semantic properties of the relations in KGs. In this paper, we propose a knowledge-enhanced approach for KG embedding learning that leverages the explicit knowledge about the relation properties. The knowledge is used to enhance the training data and guide the model on data selection during each training iteration. We conduct a series of experiments on datasets WN18RR and FB15k-237. The experiment results show that our method outperforms the previous state-of-the-art models.