论文标题
关于知识图完成的图形神经网络的调查
A Survey on Graph Neural Networks for Knowledge Graph Completion
论文作者
论文摘要
知识图越来越受到各种下游任务（例如问答和信息检索）的流行。但是，知识图通常是不完整的，因此导致性能差。结果，知识基础完成的任务引起了很多兴趣。最近，图形神经网络已用于捕获这些知识图中固有存储的结构信息，并已被证明可以在各种数据集中实现SOTA性能。在这项调查中，我们了解拟议方法论的各种优势和劣势，并试图在该领域找到需要进一步研究的新令人兴奋的研究问题。
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.
