MoEMeta：基于少量样本关系学习的专家混合元学习
MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
AI总结：
MoEMeta通过混合专家模型和任务定制适应机制，提升少样本关系学习的泛化与适应能力。
摘要：
少样本知识图谱关系学习旨在仅给定有限数量的训练示例的情况下进行推理。尽管现有方法大多采用元学习框架以实现快速适应新关系，但它们有两个关键缺陷。首先，它们在孤立状态下学习关系元知识，无法捕捉跨任务共享的共同关系模式。第二，它们难以有效整合局部、任务特定的上下文，这对于快速适应至关重要。为了解决这些限制，我们提出了MoEMeta，一种新的元学习框架，将全局共享知识与任务特定的上下文区分开来，以实现有效的模型泛化和快速适应。MoEMeta引入了两个关键创新：(i) 一个混合专家（MoE）模型，学习全局共享的关系原型以增强泛化能力，(ii) 一个任务定制的适应机制，捕捉局部上下文以实现快速任务特定的适应。通过平衡全局泛化与局部适应性，MoEMeta显著推进了少样本关系学习。在三个KG基准上的广泛实验和分析显示，MoEMeta在所有基准上均优于现有基线，实现了最先进的性能。
英文摘要：
Few-shot knowledge graph relational learning seeks to perform reasoning over relations given only a limited number of training examples. While existing approaches largely adopt a meta-learning framework for enabling fast adaptation to new relations, they suffer from two key pitfalls. First, they learn relation meta-knowledge in isolation, failing to capture common relational patterns shared across tasks. Second, they struggle to effectively incorporate local, task-specific contexts crucial for rapid adaptation. To address these limitations, we propose MoEMeta, a novel meta-learning framework that disentangles globally shared knowledge from task-specific contexts to enable both effective model generalization and rapid adaptation. MoEMeta introduces two key innovations: (i) a mixture-of-experts (MoE) model that learns globally shared relational prototypes to enhance generalization, and (ii) a task-tailored adaptation mechanism that captures local contexts for fast task-specific adaptation. By balancing global generalization with local adaptability, MoEMeta significantly advances few-shot relational learning. Extensive experiments and analyses on three KG benchmarks show that MoEMeta consistently outperforms existing baselines, achieving state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
