Before revision: algorithm Compute entity and relation embeddings is input: The training set , entity set , relation set , embedding dimension output: Entity and relation embeddings initialization: the entities and relations embeddings (vectors) are randomly initialized while stop condition do // From the training set randomly sample a batch of size b for each in do // sample a corrupted fact or triple end for Update embeddings by minimizing the loss function end while Performance indicators These indexes are often used to measure the embedding quality of a model.
After revision: algofithm Compute entity and relation embeddings is input: The training set , entity set , relation set , embedding dimension output: Entity and relation embeddings initialization: the entities and relations embeddings (vectors) are randomly initialized while stop condition do // From the training set randomly sample a batch of size b for each in do // sample a corrupted fact or triple end for Update embeddings by minimizing the loss function end while Performance indicators These indexes are often used to measure the embedding quality of a model.