Learning Transferable Topological Meta-Knowledge via Subgraph Embedding for Inductive Link Prediction

Inductive link prediction over knowledge graphs aims to generalize relational reasoning to entities that are entirely unseen during training. Existing methods alleviate the identity-binding limitation of conventional knowledge graph models, but remain challenged by cross-graph structural variation and the propagation of query-irrelevant information. To address these issues, we propose TOME (TOpological Meta-knowledge Extractor), a topology-based framework for inductive link prediction. TOME couples two complementary mechanisms: episodic meta-training over localized subgraphs, which aligns training with entity-disjoint graph transfer, and query-conditioned in-situ message passing, which incorporates the query relation into both message formulation and attention before neighborhood aggregation. This coupling enables TOME to learn transferable structural reasoning while suppressing irrelevant signals during message construction, without relying on target-entity-specific embeddings. Extensive experiments on WN18RR, FB15k-237, and NELL-995 demonstrate that TOME achieves strong overall performance in both inductive and transductive link prediction, outperforming topology-based baselines on most inductive and all transductive dataset–metric combinations. Ablation studies show that episodic meta-training and query-conditioned message passing make complementary contributions, with message-level query conditioning providing a substantial improvement. Further sparsity analyses support the robustness of TOME, while a qualitative case study illustrates its interpretable structural reasoning. These results demonstrate that coupling transfer-oriented meta-training with query-conditioned in-situ reasoning is an effective approach to entity-independent link prediction over unseen knowledge graphs.