731@2022@IJCAI

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#1 Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract) [PDF] [Copy] [Kimi] [REL]

Authors: Mehdi Ali ; Max Berrendorf ; Mikhail Galkin ; Veronika Thost ; Tengfei Ma ; Volker Tresp ; Jens Lehmann

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. Our code is available at https://github.com/mali-git/hyper_relational_ilp.