HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

Abstract

Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely *novel entities* (i.e., nodes unseen during training). Existing methods for inductive link prediction with knowledge hypergraphs assume a fixed relational vocabulary and, as a result, cannot generalize to knowledge hypergraphs with *novel relation types* (i.e., relations unseen during training). Inspired by knowledge graph foundation models, we propose HYPER as a foundation model for link prediction, which can generalize to *any knowledge hypergraph*, including novel entities and novel relations. Importantly, HYPER can learn and transfer across different relation types of *varying arities*, by encoding the entities of each hyperedge along with their respective positions in the hyperedge. To evaluate HYPER, we construct 16 new inductive datasets from existing knowledge hypergraphs, covering a diverse range of relation types of varying arities. Empirically, HYPER consistently outperforms all existing methods in both node-only and node-and-relation inductive settings, showing strong generalization to unseen, higher-arity relational structures.