  Knowledge Graphs are increasingly becoming popular for a variety of
downstream tasks like Question Answering and Information Retrieval. However,
the Knowledge Graphs are often incomplete, thus leading to poor performance. As
a result, there has been a lot of interest in the task of Knowledge Base
Completion. More recently, Graph Neural Networks have been used to capture
structural information inherently stored in these Knowledge Graphs and have
been shown to achieve SOTA performance across a variety of datasets. In this
survey, we understand the various strengths and weaknesses of the proposed
methodology and try to find new exciting research problems in this area that
require further investigation.
