Conjugate Relation Modeling for Few-Shot Knowledge Graph Completion
This addresses data sparsity and complex relational patterns in knowledge graphs for applications like recommendation systems, but appears incremental as it builds on existing few-shot methods.
The paper tackles the problem of few-shot knowledge graph completion by proposing a novel framework for conjugate relation modeling, which achieves superior performance over state-of-the-art methods on three benchmarks.
Few-shot Knowledge Graph Completion (FKGC) infers missing triples from limited support samples, tackling long-tail distribution challenges. Existing methods, however, struggle to capture complex relational patterns and mitigate data sparsity. To address these challenges, we propose a novel FKGC framework for conjugate relation modeling (CR-FKGC). Specifically, it employs a neighborhood aggregation encoder to integrate higher-order neighbor information, a conjugate relation learner combining an implicit conditional diffusion relation module with a stable relation module to capture stable semantics and uncertainty offsets, and a manifold conjugate decoder for efficient evaluation and inference of missing triples in manifold space. Experiments on three benchmarks demonstrate that our method achieves superior performance over state-of-the-art methods.