Temporal Knowledge

Temporal knowledge research focuses on understanding and modeling how information changes over time, aiming to improve predictions and reasoning about dynamic systems. Current efforts concentrate on integrating temporal information into various models, including large language models (LLMs), neural-symbolic systems, and graph-based approaches, often leveraging techniques like retrieval-augmented generation, attention mechanisms, and knowledge distillation to enhance accuracy and efficiency. This field is crucial for advancing applications across diverse domains, such as traffic prediction, event forecasting, video understanding, and activity recognition, by enabling more accurate and adaptable systems that account for temporal dependencies in data.