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A Survey on Graph Neural Networks for Knowledge Graph Completion
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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.
Forward citations
Cited by 3 Pith papers
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Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
A two-level taxonomy (KG pipeline stages × GNN architectures) systematically reviews GNN methods for knowledge-graph construction, embedding, reasoning, and applications.
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Introduction to Graph Neural Networks for Machine Learning Engineers
A tutorial survey of graph neural networks using an encoder-decoder framework, accompanied by an experimental study of hyperparameters and graph homophily on node classification.
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