Temporal Knowledge Graph

Temporal Knowledge Graphs (TKGs) extend traditional knowledge graphs by incorporating temporal information, aiming to model the dynamic evolution of real-world knowledge and predict future events based on historical data. Current research focuses on developing sophisticated reasoning models, often employing hybrid approaches that combine geometric embeddings (e.g., Euclidean, hyperbolic) with neural networks (e.g., Transformers, Graph Neural Networks) or large language models (LLMs) to capture complex semantic and hierarchical relationships across time. These advancements are improving the accuracy of tasks like link prediction and question answering, with applications ranging from career trajectory prediction to anomaly detection in dynamic systems.