KGE Algorithms

<p> An updated method for link prediction that uses a regularization factor that models relation argument types</p>

<strong>Abstract (Kotnis and Nastase, 2017):</strong> </br>
Learning relations based on evidence from knowledge repositories
relies on processing the available relation instances. Knowledge
repositories are not balanced in terms of relations or entities – there
are relations with less than 10 but also thousands of instances, and
entities involved in less than 10 but also thousands of relations.
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 hits@N, N = 1, 3, 10. Furthermore, we
discover scenarios that significantly impact the effectiveness of the
type regularizer.