Improving Inductive Link Prediction Using Hyper-Relational Facts
Abstract
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only consider triple-based \glspl{kg}, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied. In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. Our code is available at \url{https://github.com/mali-git/hyper_relational_ilp}.
Cite
@article{arxiv.2107.04894,
title = {Improving Inductive Link Prediction Using Hyper-Relational Facts},
author = {Mehdi Ali and Max Berrendorf and Mikhail Galkin and Veronika Thost and Tengfei Ma and Volker Tresp and Jens Lehmann},
journal= {arXiv preprint arXiv:2107.04894},
year = {2021}
}