Learning Knowledge Graph Embeddings with Type Regularizer
Bhushan Kotnis, Vivi Năstase · 2017
Learning relations based on evidence from knowledge repositories rely 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. Tested on Freebase, a frequently used benchmarking dataset for link/path predicting tasks, we note increased performance compared to the baseline model in terms of mean reciprocal rank and [email protected], N = 1, 3, 10. Furthermore, we discover scenarios that significantly impact the effectiveness of the type regularizer.