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 detail 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.
Blogger's Review: This paper provides a systematic framework and in-depth analysis of the integration of knowledge graphs and graph neural networks, especially in terms of categorization and model review, which is of significant academic value and practical relevance. As the application of knowledge graphs becomes more widespread, the advantages of GNNs will become increasingly prominent and warrant continued attention.