MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Han Wu, Jie Yin
Few-shot knowledge graph (KG) relational learning focuses on performing reasoning over relations with only a limited number of training examples. Most current approaches follow a meta-learning framework but suffer from two key pitfalls. First, they typically learn relation meta-knowledge in isolation, failing to capture common relational patterns shared among related tasks. Second, they overlook local, task-specific information crucial for rapid adaptation to each new task. To address these limitations, we propose MoEMeta, a novel meta-learning framework with two key innovations: (1) a mixture-of-experts model proposed to learn relational prototypes shared across tasks, enabling better generalization, and (2) a task-tailored adaptation mechanism designed to facilitate local adaptation to account for task-specific contexts. By balancing global generalization with local adaptability, MoEMeta significantly improves few-shot relational learning. Extensive experiments and ablation studies on three few-shot KG benchmarks demonstrate MoEMeta outperforms existing baselines, achieving state-of-the-art performance.