## 3 KGs Learning
### Dynamic and Task-Specific Learning  
#### 3.3.1 Temporal Knowledge Graphs (TKG)  
Research is being conducted on GNNs that learn temporal knowledge graphs (TKGs) that change over time, enabling the inference of future facts from past data. When tracking structural changes over time, the GNN layers update the node representations by integrating the current timestamp graph with past (or neighboring) timestamps. For example, SFTe [112] predicts future interactions by embedding the structure, factuality, and temporality individually.  
To adapt quickly, even with few-shot training samples for new relations or entities, research is exploring GNNs with meta-learning to dynamically adjust parameters [113].  
Models like MTRN (task-related network based on meta-learning) employ specialized components, such as neighbor-aware encoders and self-attention encoders, to sensitively respond to task transitions. Knowledgeberba [114] proposed an embedding method that optimizes the model performance by leveraging the algebraic structure of KGs to enhance global consistency. Dynamic relation learning for link prediction in knowledge hypergraphs [115] improves the link prediction performance in hypergraphs through dynamic relation learning.  
T-GAE [116] utilizes temporal information in graphs to learn the interactions between nodes. It employs a graph attention mechanism to integrate spatiotemporal dependencies, which allows the model to capture dynamic changes in temporal graphs, predict future interactions from past data and handle complex temporal patterns efficiently. The Bayesian hierarchical graph neural network (BHGNN) [117] incorporates uncertainty in graph data into the learning process to produce highly reliable results. It is particularly effective in tasks where reliability is crucial, such as industrial fault diagnosis, in which uncertainty feedback is used to simultaneously improve prediction accuracy and model confidence. This approach contributes to error detection and refinement in several scenarios.  
CDRGN-SDE [118] employs a cross-dimension network to model interactions across multiple dimensions when learning temporal data. The model captures changing graph structures along the temporal axis and learns complex dependencies in temporal graphs to make accurate predictions. This enables effective learning, even in large-scale temporal graphs. [PERSON]. [119] proposed a model that simultaneously learns multiple graph structures along the temporal axis and infers relationships and patterns in TKGs. This model efficiently captures time-dependent patterns and reflects interactions across the past, present, and future.  
Dynamic learning approaches in temporal knowledge graphs not only adapt to evolving data but also help mitigate issues such as hallucinated relationships. For instance, models like T-GAE adjust node representations over time to reduce the occurrence of spurious or unsupported links. For example, models like T-GAE [116] incorporate temporal-structural adaptations to adjust node representations over time, reducing the occurrence of spurious or unsupported relations. Additionally, frameworks that integrate reinforcement learning [117] provide mechanisms to continuously refine the graph structure, ensuring that errors due to hallucination are minimized in real-world applications.