Knowledge graphs are a way to represent a large number of relational facts coming from real-world knowledge. Since that knowledge is generally incomplete, a vast research area, known as Link Prediction is devoted to infer possible unknown facts based on the existing ones. We focus on a recent state-of-the-art linear model called TuckER that was introduced for the task of link prediction by Balazevich et al. (Balazevic et al., 2019). In this project, we propose ways to incorporate background knowledge about symmetric and asymmetric relations. We show that our model performs better than the TuckER model on those relations while requiring half of the parameters.