A comprehensive survey of graph neural networks for knowledge graphs

Junsong, Wang and Zi, Ye and Goh, Ong Sing and Jaya Kumar, Yogan and Song, Fengyan (2022) A comprehensive survey of graph neural networks for knowledge graphs. IEEE Access, 35 (12). pp. 12350-12368. ISSN 2169-3536

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Abstract

The Knowledge graph, a multi-relational graph that represents rich factual information among entities of diverse classifications, has gradually become one of the critical tools for knowledge management. However, the existing knowledge graph still has some problems which form hot research topics in recent years. Numerous methods have been proposed based on various representation techniques. Graph Neural Network, a framework that uses deep learning to process graph structured data directly, has significantly advanced the state-of-the-art in the past few years. This study firstly is aimed at providing a broad, complete as well as comprehensive overview of GNN-based technologies for solving four different KG tasks, including link prediction, knowledge graph alignment, knowledge graph reasoning, and node classification. Further, we also investigated the related artificial intelligence applications of knowledge graphs based on advanced GNN methods, such as recommender systems, question answering, and drug-drug interaction. This review will provide new insights for further study of KG and GNN.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Distributed embedding, Graph neural network, Knowledge graph, Representation learning
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	16 Jan 2024 10:50
Last Modified:	16 Jan 2024 10:50
URI:	http://eprints.utem.edu.my/id/eprint/27070
Statistic Details:	View Download Statistic

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