## 3 KGs Learning
### Graph Neural Network (GNN)-Based Learning
#### 3.2.1 Key Features and Application Highlights How GNNs  
Graph Neural Networks (GNNs) learn embeddings for each node by iteratively aggregating and transforming the structure of the nodes, edges, and their neighbors using message-passing techniques. When applied to knowledge graphs, GNNs exhibit the following characteristics: First, heterogeneous graphs with diverse types of nodes and relationships can be flexibly handled using techniques like meta-paths or attention mechanisms. Second, while iterative structural learning enables information from neighboring nodes to propagate into deeper layers, allowing higher-order structure learning, caution is required to avoid over-smoothing. Multi-Grained Semantics-Aware Graph Neural Networks [88] proposed a GNN that effectively learns the rich semantic information of knowledge graphs by considering multi-grained semantics. Furthermore, the Graph Relearn Network [89] introduced methods to mitigate performance fluctuations in GNNs and improve prediction accuracy. Recent work has further expanded the application scope of GNNs. For instance, ref. [90] leveraged spatiotemporal scene graph embeddings--where GNNs are combined with LSTM layers--to predict autonomous vehicle collisions by capturing dynamic inter-object relationships in complex traffic scenes. Similarly, ref. [91] demonstrated that integrating Graph Convolutional Neural Networks with Label Propagation can significantly enhance node classification performance by adaptively learning edge weights based on node label information. The GNN-based Imbalanced Node Classification Model (GNN-INCM) [92] is designed to address class imbalance issues by applying loss balancing along with node sampling strategies to enhance performance in minority classes. Specifically, it selects nodes based on importance and utilizes the structural characteristics of graph networks to effectively learn from minority class data. This leads to a robust node classification performance, even in datasets with severe class imbalances. INS-GNN [93], which integrates self-supervised learning into GNNs, ensures stable node classification performance on imbalanced datasets. By autonomously learning the characteristics of graph data, this model alleviates the problem of under-learning in minority class nodes, achieving practical performance improvements in various real-world networks. GNNs have emerged as a powerful paradigm for learning structured graph data by leveraging both node attributes and underlying graph topology to generate informative embeddings [94]. By aggregating information from neighbors, GNNs capture complex dependencies within graphs, making them highly effective for tasks like node classification and link prediction. The Graph Convolutional Network (GCN), an early GNN framework, introduced convolution-like operations tailored for graph-structured data [95]. The Graph Attention Network (GAT) architecture refines this process by incorporating an attention mechanism to assign adaptive weights to neighbor information [94]. The same GAT architecture leverages a specialized relational aggregator that captures nuanced interactions associated with various entity and relationship types, making it well-suited for heterogeneous graphs [94; 96]. The Dynamic Representation of Relations and global information (DRR-GAT) [97] extends the existing GAT by applying dynamic relation-specific weights, effectively modeling complex graph structures. Iterative Graph Self-Distillation (IGSD) [98] employs an iterative self-distillation technique to enhance the prediction stability of GNN models by refining the latent representation of nodes. At each iteration, it generates new embeddings to improve the model prediction accuracy,particularly in large graph datasets, reducing the model variance and delivering more precise results. Beyond general graph analytics, GNNs serve as the cornerstone of knowledge graph (KG) learning, supporting various tasks such as entity classification, relationship prediction, and KG completion [99; 100].