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.

翻译：知识图在诸如问答和信息检索等一系列下游任务中越来越受欢迎,然而,知识图往往不完整,导致业绩不佳,因此对完成知识库的任务产生了很大兴趣,最近,图神经网络被用来捕捉这些知识图中固有的结构信息,并显示可在各种数据集中取得SOTA的性能。在本次调查中,我们了解拟议方法的各种优缺点,并试图找出这一领域需要进一步研究的令人振奋的新研究问题。