Knowledge Graph Embeddings 2D Map: TransE vs RotatE Link Prediction

A small knowledge graph of continents, countries and capital cities lives here as points on a 2D embedding map, the same way production knowledge-graph-embedding systems place entities in vector space. Two relations connect them — capital_of and part_of — and two scoring models compete to predict missing links: TransE, which represents a relation as a translation vector added to the head, and RotatE, which represents it as a rotation about the origin. Pick a head entity and a relation, switch between the two models, and watch the predicted point, the ranked candidate list and the true tail's rank change live, making visible exactly why the choice of relation operator matters for real link prediction.