@inproceedings{discovery10154327,
            year = {2021},
       booktitle = {Proceedings of the 3rd Conference on Automated Knowledge Base Construction (AKBC)},
           title = {Relation Prediction as an Auxiliary Training Objective
for Improving Multi-Relational Graph Representations},
       publisher = {Automated Knowledge Base Construction (AKBC)},
           month = {October},
            note = {This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Virtual conference},
             url = {https://www.akbc.ws/2021/cfp/},
          author = {Chen, Yihong and Minervini, Pasquale and Riedel, Sebastian and Stenetorp, Pontus},
        abstract = {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. Moreover,
we observe that the proposed objective is especially effective on highly multi-relational datasets,
i.e. datasets with a large number of predicates, and generates better representations when larger
embedding sizes are used.},
        keywords = {cs.CL, cs.CL, cs.AI}
}