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.
@article{arxiv.2510.22656,
title = {Conjugate Relation Modeling for Few-Shot Knowledge Graph Completion},
author = {Zilong Wang and Qingtian Zeng and Hua Duan and Cheng Cheng and Minghao Zou and Ziyang Wang},
journal= {arXiv preprint arXiv:2510.22656},
year = {2026}
}