论文标题
关系依赖性的对比度学习，并通过群集抽样进行感应关系预测
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction
论文作者
论文摘要
关系预测是一项针对知识图完成的任务，旨在预测实体之间的缺失关系。最新的基于子图的归纳关系预测模型已受到越来越多的关注，这可以根据围绕候选三胞胎的提取子图预测未见实体的关系。但是，由于他们预测看不见的关系的残疾，它们并不是完全归纳的。此外，他们没有充分关注关系的作用，因为它们仅取决于模型学习参数化关系嵌入，这导致对长尾关系的预测不准确。在本文中，我们引入了依赖关系的对比学习（recole），以进行归纳关系预测，该预测以基于聚类算法的新型抽样方法适应对比度学习，以增强关系的作用并提高不看到关系的概括能力。 Recole不是直接学习嵌入关系中的关系，而是将基于GNN的预训练的编码器分配给每个关系，以增强关系的影响。基于GNN的编码器通过对比度学习进行了优化，从而确保了长尾关系的令人满意的表现。此外，群集抽样方法将介绍的能力与处理看不见的关系和实体的能力。实验结果表明，对常用归纳数据集的repole优于最先进的方法。
Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing attention, which can predict relation for unseen entities based on the extracted subgraph surrounding the candidate triplet. However, they are not completely inductive because of their disability of predicting unseen relations. Moreover, they fail to pay sufficient attention to the role of relation as they only depend on the model to learn parameterized relation embedding, which leads to inaccurate prediction on long-tail relations. In this paper, we introduce Relation-dependent Contrastive Learning (ReCoLe) for inductive relation prediction, which adapts contrastive learning with a novel sampling method based on clustering algorithm to enhance the role of relation and improve the generalization ability to unseen relations. Instead of directly learning embedding for relations, ReCoLe allocates a pre-trained GNN-based encoder to each relation to strengthen the influence of relation. The GNN-based encoder is optimized by contrastive learning, which ensures satisfactory performance on long-tail relations. In addition, the cluster sampling method equips ReCoLe with the ability to handle both unseen relations and entities. Experimental results suggest that ReCoLe outperforms state-of-the-art methods on commonly used inductive datasets.
