Towards Hierarchical Invariant Semantics for Inductive Knowledge Graph Reasoning

Inductive Knowledge Graph Reasoning (IKGR) aims to infer missing information for unseen entities and relations given an observed Knowledge Graph (KG). However, most existing GNN-based methods suffer from limited generalization and poor extrapolation due to indiscriminate message passing mechanism, which entangles task-relevant signals with spurious correlations confined to the training environment. In the inductive settings, such spurious patterns dilute invariant learning and significantly degrade performance. To address this challenge, we propose HiKGR, a novel framework that models Hierarchical invariant semantics for inductive Knowledge Graph Reasoning, thereby improving generalization and extrapolation. Specifically, we provide an analysis that the learned representation is prone to spurious associations without explicit invariant constraints. Then we design a multi-environment generator that simulates diverse KG domains, and enhance extrapolation by exploring invariant semantic factors at hierarchical relation, entity, and subgraph levels. Further, we develop a bi-level optimization strategy to jointly encourage cross-environment invariance while preserving domain diversity. Extensive experiments on real-world datasets demonstrate that the proposed HiKGR consistently outperforms state-of-the-art methods.