Conjugate relation modeling for few-shot knowledge graph completion

Wang, Zilong, Zeng, Qingtian, Duan, Hua, Cheng, Cheng, Zou, Minghao and Wang, Ziyang 2026. Conjugate relation modeling for few-shot knowledge graph completion. Presented at: 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 3-8 May 2026. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics Speech and Signal Processing. IEEE; 1999, pp. 4446-4450. 10.1109/icassp55912.2026.11461311

Full text not available from this repository.

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.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE; 1999
ISBN:	979-8-3315-6702-6
ISSN:	1520-6149
Last Modified:	08 May 2026 09:37
URI:	https://orca.cardiff.ac.uk/id/eprint/186870

Actions (repository staff only)

Edit Item