Continual Relation Extraction

Continual relation extraction (CRE) focuses on building machine learning models that can incrementally learn new relationships between entities in data streams without forgetting previously learned relationships—a challenge known as catastrophic forgetting. Current research emphasizes techniques like contrastive learning, memory replay mechanisms (often refined to avoid overfitting to old data), and strategies to improve the robustness of learned representations against similar, but distinct, relations. These advancements are crucial for building more adaptable and robust knowledge graphs from continuously evolving real-world data, impacting fields like information retrieval and knowledge base construction.