Home | Publications | ABG+22

Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)

MCML Authors

Max Berrendorf

Dr.

* Former Member

→ Group Volker Tresp
Database Systems, Data Mining and AI

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

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 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.

inproceedings

IJCAI-ECAI 2022

Best paper track at the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence. Vienna, Austria, Jul 23-29, 2022.