Knowledge Graph Embeddings

Knowledge Graph Embeddings (KGE) is a technique that maps entities and relations in a knowledge graph to a low-dimensional vector space, aiming to capture their semantic information through learning distributed representations of entities and relations. The goal is to optimize the embedding vectors so that they accurately reflect the structure and logical relationships of the knowledge graph in the vector space, thereby improving the accuracy of knowledge reasoning and prediction. KGE has significant value in applications such as recommendation systems, semantic search, link prediction, and knowledge completion, effectively enhancing the intelligence level and user experience of these systems.