Entity Agnostic

Entity-agnostic methods aim to create models that perform well across diverse entities without requiring separate training or parameters for each. Current research focuses on developing efficient representation learning techniques, often employing shared subgraphs or universal encoders to generalize across entities, rather than learning individual embeddings for each. This approach addresses the scalability challenges of traditional methods, particularly in knowledge graph completion and biomedical entity linking, where the number of entities can be vast. The resulting parameter efficiency and improved generalization capabilities have significant implications for resource-constrained applications and the development of more robust and adaptable AI systems.