Knowledge graph embeddings map entities and relations into continuous vector spaces.

An embedding assigns each entity and relation a dense numerical representation in a low-dimensional space. The aim is not merely compression: the geometry should preserve enough semantic and structural information to support downstream tasks such as link prediction, entity clustering, and node classification. The representation space, scoring function, interaction model, and optional auxiliary information define the main design choices of an embedding system.