Learning Knowledge Graph Embeddings with Type Regularizer
  Learning relations based on evidence from knowledge bases relies on
processing the available relation instances. Many relations, however, have
clear domain and range, which we hypothesize could help learn a better, more
generalizing, model. We include such information in the RESCAL model in the
form of a regularization factor added to the loss function that takes into
account the types (categories) of the entities that appear as arguments to
relations in the knowledge base. We note increased performance compared to the
baseline model in terms of mean reciprocal rank and hits@N, N = 1, 3, 10.
Furthermore, we discover scenarios that significantly impact the effectiveness
of the type regularizer.
