Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

Understand

Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).

• In this paper, we propose a new self-supervised training objective for multi-relational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective.
• The new training objective contains not only terms for predicting the subject and object of a given triple, but also a term for predicting the relation type.
• We analyse how this new objective impacts multi-relational learning in KBC: experiments on a variety of datasets and models show that relation prediction can significantly improve entity ranking, the most widely used evaluation task for KBC, yielding a 6.1% increase in MRR and 9.9% increase in Hits@1 on FB15k-237 as well as a 3.1% increase in MRR and 3.4% in Hits@1 on Aristo-v4.