用迁移学习提升少样本知识图谱补全效果
TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion
- 基于任务间关联性设计迁移学习框架，共享跨关系知识
- 在少量样本下对新关系预测准确率显著提升
- 适合处理长尾分布关系的少样本知识补全场景
知识图谱在各类应用中广泛存在，但多数现实世界知识图谱不完整，且关系呈现长尾分布——多数关系仅由少量训练三元组表示。为此，少样本知识图谱补全（few-shot KG completion）旨在仅有少量训练三元组时，对涉及新关系的三元组进行准确预测。现有方法通常孤立学习每个关系，忽视不同任务间的关联及先前任务中的有用信息。本文提出一种基于迁移学习的少样本知识图谱补全方法（TransNet），通过建模任务间关系，将相似任务的知识有效迁移到当前任务，从而提升性能。结合元学习机制，TransNet 能够泛化至未见过的新关系。在基准数据集上的大量实验表明，其优于现有最先进方法。代码已公开于 https://github.com/lihuiliullh/TransNet/tree/main。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performance on downstream tasks. Additionally, the relationships in real-world knowledge graphs often follow a long-tail distribution, meaning that most relations are represented by only a few training triplets. To address these challenges, few-shot learning has been introduced. Few-shot KG completion aims to make accurate predictions for triplets involving novel relations when only a limited number of training triplets are available. Although many methods have been proposed, they typically learn each relation individually, overlooking the correlations between different tasks and the relevant information in previously trained tasks. In this paper, we propose a transfer learning-based few-shot KG completion method (TransNet). By learning the relationships between different tasks, TransNet effectively transfers knowledge from similar tasks to improve the current task's performance. Furthermore, by employing meta-learning, TransNet can generalize effectively to new, unseen relations. Extensive experiments on benchmark datasets demonstrate the superiority of TransNet over state-of-the-art methods. Code can be found at https://github.com/lihuiliullh/TransNet/tree/main
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