The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions. Most of the current methods rely on learning every possible scenario which is reliant on expensive data annotation. Moreover, such annotations can introduce unintended bias which makes systems focus more on the bias than the actual task. In this work, we propose Knowledge Triplet Learning, a self-supervised task over knowledge graphs. We propose methods of how to use such a model to perform zero-shot QA and our experiments show considerable improvements over large pre-trained generative models.