Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion

arXiv:2403.04521v1 Announce Type: new
Abstract: Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better …

abstract arxiv cs.cl facts few-shot few-shot learning graph graph neural network knowledge knowledge graph network neural network query reference relational type uncertainty