Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
This paper proposes a novel two-level taxonomy for GNN-based knowledge graph technologies, covering construction, embedding, reasoning, and applications, and reviews various GNN models, discussing their strengths, limitations, and future directions.
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[Submitted on 12 May 2026]
Title:Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
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Abstract: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.
Comments: Recently Accepted for publication in ACM Computing Surveys. This is the accepted manuscript version, and the final published version available at ACM Computing Surveys:this https URL. Paper list at Github: this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
MSC classes: 68T30, 68T07, 68R10
ACM classes: I.2.4; I.2.6; H.2.8; A.1
Cite as: arXiv:2607.09666 [cs.LG]
(or arXiv:2607.09666v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.09666
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3814608
DOI(s) linking to related resources
Submission history
From: Chengcheng Sun [view email] [v1] Tue, 12 May 2026 01:56:00 UTC (1,232 KB)
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