Knowledge Graph Embedding

A knowledge graph is a structured representation of knowledge—entities (Alice, Google, CEO) connected by relationships (Alice works_at Google, Alice position CEO). A knowledge graph embedding converts this discrete graph into continuous vector space—each entity and relationship becomes a d-dimensional vector. The embedding preserves graph structure: if two entities are connected in the graph, their embeddings should be close. Methods (TransE, DistMult, RotatE) learn embeddings by minimizing a scoring function. The resulting embeddings enable downstream tasks: link prediction (guess missing relationships), entity similarity (find similar entities), semantic search.