{"ID":2850031,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22656","arxiv_id":"2510.22656","title":"Conjugate Relation Modeling for Few-Shot Knowledge Graph Completion","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.22656","url_pdf":"https://arxiv.org/pdf/2510.22656v2","authors":"[\"Zilong Wang\",\"Qingtian Zeng\",\"Hua Duan\",\"Cheng Cheng\",\"Minghao Zou\",\"Ziyang Wang\"]","published":"2025-10-26T12:38:23Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Diffusion Model\"]","has_code":false}