发表机构
School of Safety Engineering, China University of Mining and Technology; School of Computer Science and Technology, China University of Mining and Technology; State Key Laboratory of Coal Mine Disaster Prevention and Control, China University of Mining and Technology; Department of Computer Science, University of Illinois at Chicago（中国矿业大学安全工程学院; 中国矿业大学计算机科学与技术学院; 中国矿业大学煤矿灾害防治国家重点实验室; 伊利诺伊大学芝加哥分校计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述针对知识图谱技术流程中基于GNN方法缺乏系统综述的问题，提出两级分类框架，分析GNN技术优势，详细回顾相关模型并总结优缺点，还讨论挑战与未来研究方向。
AI 中文摘要
图神经网络（GNNs）因其对图结构数据建模的内在能力，已成为知识图谱（KGs）中的强大范式。然而，缺乏对整个知识图谱技术流程中基于GNN方法的系统综述。为填补这一空白，我们首先为基于GNN的知识图谱技术提出了一个新颖的两级分类框架：知识图谱技术流程和基于GNN的视角。知识图谱技术流程涵盖知识图谱构建、嵌入、推理及应用。基于GNN的视角用GNN模型对知识图谱技术进行新分类。接着，基于知识图谱生命周期中不同任务的特点分析GNN技术优势。还详细回顾了基于所提分类法的各类GNN知识图谱模型，总结优缺点。最后讨论未解决的挑战并概述未来研究的有前景方向。
英文摘要
Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the entire knowledge graph technologies pipeline. To address this gap, we first propose a novel two-level taxonomy framework for GNN-based knowledge graph technologies: the KG technologies pipeline and GNN-based perspective. Specifically, the knowledge graph technologies pipeline covers knowledge graph construction, knowledge graph embedding, knowledge reasoning and knowledge graph applications. Meanwhile, the GNN-based perspective provides a new categorization of knowledge graph technologies with GNN models, such as GCN, GAT, and HGNN. Then, we analyze the advantages of GNN technology based on the characteristics of different tasks in the knowledge graph lifecycle. Furthermore, we detailed review various GNN-based models for knowledge graph following the proposed taxonomy, and summarize strengths and limitations. Finally, we discuss unresolved challenges and outline promising directions for future research.
CommentsRecently Accepted for publication in ACM Computing Surveys. This is the accepted manuscript version, and the final published version available at ACM Computing Surveys:https://doi.org/10.1145/3814608. Paper list at Github: https://github.com/sunxiaobei/awesome-gnn-based-knowledge-graphs
DOI：10.1145/3814608
