Relational Embeddings

Relational embeddings represent relationships between entities (e.g., words, objects, individuals) as low-dimensional vectors, aiming to capture complex relational structures within data like knowledge graphs and social networks. Current research focuses on improving embedding quality through techniques like geometric modeling, knowledge distillation for efficient federated learning, and incorporating rule-based reasoning or attention mechanisms to enhance model expressiveness and generalization. These advancements are impacting various fields, enabling improved performance in tasks such as link prediction, relation extraction, and question answering, as well as providing more interpretable and robust models for diverse applications.