A Survey on Graph Neural Networks for Knowledge Graph Completion
This is an incremental survey that synthesizes existing research to identify gaps and future directions for improving knowledge graph completion, benefiting researchers and practitioners in fields like question answering and information retrieval.
This survey examines the use of Graph Neural Networks (GNNs) for Knowledge Graph Completion, addressing the problem of incomplete knowledge graphs that hinder downstream tasks, and highlights that GNNs have achieved state-of-the-art performance across various datasets.
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.