Inductive Link Prediction Using Hyper-Relational Facts
Authors
Authors
- Veronika Thost
- Mehdi Ali
- Mikhail Galkin
- Tengfei Ma
- Max Berrendorf
- Volker Tresp
- Jens Lehmann
Authors
- Veronika Thost
- Mehdi Ali
- Mikhail Galkin
- Tengfei Ma
- Max Berrendorf
- Volker Tresp
- Jens Lehmann
Published on
07/29/2022
For many years, link prediction on knowledge. graphs 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 KGs, 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.
Please cite our work using the BibTeX below.
@inproceedings{ijcai2022p731,
title = {Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)},
author = {Ali, Mehdi and Berrendorf, Max and Galkin, Mikhail and Thost, Veronika and Ma, Tengfei and Tresp, Volker and Lehmann, Jens},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5259--5263},
year = {2022},
month = {7},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2022/731},
url = {https://doi.org/10.24963/ijcai.2022/731},
}