针对知识图谱少样本关系学习，提出上下文感知的高效适配器方法
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
- 引入轻量级适配器模块，实现关系特异性参数高效微调
- 融合目标关系上下文信息，提升元学习适应能力
- 在3个基准数据集上优于现有方法，适合知识图谱补全场景
知识图谱在实际应用中至关重要，但常因缺少关系而存在不完整性。为在仅有少量训练样本的情况下预测新关系实例，少样本关系学习方法应运而生，通常采用元学习技术。然而，现有方法假设元测试中的新关系与元训练中的基础关系独立同分布，这在实践中未必成立。为此，我们提出RelAdapter，一种面向知识图谱少样本关系学习的上下文感知适配器，旨在增强元学习中的适应过程。首先，RelAdapter配备轻量级适配器模块，以参数高效方式实现元知识的关系特异性可调适配；其次，通过引入目标关系的上下文信息，提升对每种不同关系的适应能力。在三个基准知识图谱上的大量实验验证了RelAdapter优于当前最优方法。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates relation-specific, tunable adaptation of meta-knowledge in a parameter-efficient manner. Second, RelAdapter is enriched with contextual information about the target relation, enabling enhanced adaptation to each distinct relation. Extensive experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。