Meta Relation-Aware Metric Learning Framework for Few-Shot Uncertain Knowledge Graph Completion

Uncertainty is a fundamental aspect of human knowledge, widely present in various domains such as medicine, biology, and finance. Uncertain Knowledge Graph Completion (UKGC) tasks encompass both link prediction and confidence prediction. However, the inaccessibility of high-quality knowledge results in sparse knowledge graphs, where a large number of entities exhibit relatively sparse neighborhoods. This sparsity severely hinders the performance of few-shot UKGC models, which generally rely on neighborhood aggregation to enhance entity representations. In this study, we propose a Meta relation-aware Metric learning framework designed for few-shot UKGC, named MMUC. To mitigate neighbor sparsity, MMUC introduces a relation-centric paradigm that decouples representation enhancement from background graphs, integrating an uncertainty-driven similarity measure through a learnable confidence learner to achieve robust, non-linear fusion for joint link and confidence prediction. Extensive evaluations across multiple benchmarks demonstrate MMUC's superior performance through rigorous link and confidence prediction, ablation studies, and sparsity analysis, highlighting its particular resilience to neighbor sparsity in few-shot uncertain KGs. The source code can be accessed inhttps://github.com/sienna-wxy/MMUC.