用元语义提示提升少样本关系推理能力
Meta-Semantics Augmented Few-Shot Relational Learning
- 引入元语义提示池，提取跨任务共享的高层次语义
- 动态融合元语义与具体关系信息，适应新关系
- 在真实知识图谱上验证，少样本下表现显著提升
知识图谱上的少样本关系学习旨在仅用少量训练样本进行关系推理。现有方法主要依赖特定关系信息，而忽略了知识图谱中丰富的内在语义。为填补这一空白，我们提出PromptMeta——一种将元语义与关系信息无缝结合的提示式元学习框架。该框架包含两项核心创新：(1) 元语义提示（MSP）池，可学习并整合跨任务共享的高层元语义，实现有效知识迁移与对新出现关系的适应；(2) 可学习的融合机制，能动态结合元语义与任务特异性关系信息，适配不同少样本任务。两个组件在元学习框架内与模型参数联合优化。在两个真实世界知识图谱基准上的大量实验与分析表明，PromptMeta在有限监督下有效适应新关系。
原文摘要 · Abstract (English)
Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on leveraging specific relational information, rich semantics inherent in KGs have been largely overlooked. To bridge this gap, we propose PromptMeta, a novel prompted meta-learning framework that seamlessly integrates meta-semantics with relational information for few-shot relational learning. PromptMeta introduces two core innovations: (1) a Meta-Semantic Prompt (MSP) pool that learns and consolidates high-level meta-semantics shared across tasks, enabling effective knowledge transfer and adaptation to newly emerging relations; and (2) a learnable fusion mechanism that dynamically combines meta-semantics with task-specific relational information tailored to different few-shot tasks. Both components are optimized jointly with model parameters within a meta-learning framework. Extensive experiments and analyses on two real-world KG benchmarks validate the effectiveness of PromptMeta in adapting to new relations with limited supervision.
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