Title: Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion Show Chinese title

Title: 基于注意力损失的关系感知网络用于少样本知识图谱补全

Abstract: Few-shot knowledge graph completion (FKGC) task aims to predict unseen facts of a relation with few-shot reference entity pairs. Current approaches randomly select one negative sample for each reference entity pair to minimize a margin-based ranking loss, which easily leads to a zero-loss problem if the negative sample is far away from the positive sample and then out of the margin. Moreover, the entity should have a different representation under a different context. To tackle these issues, we propose a novel Relation-Aware Network with Attention-Based Loss (RANA) framework. Specifically, to better utilize the plentiful negative samples and alleviate the zero-loss issue, we strategically select relevant negative samples and design an attention-based loss function to further differentiate the importance of each negative sample. The intuition is that negative samples more similar to positive samples will contribute more to the model. Further, we design a dynamic relation-aware entity encoder for learning a context-dependent entity representation. Experiments demonstrate that RANA outperforms the state-of-the-art models on two benchmark datasets. Show Chinese abstract

Abstract: 少样本知识图谱补全（FKGC）任务旨在通过少量样本参考实体对预测关系的未见事实。 当前方法为每个参考实体对随机选择一个负样本以最小化基于间隔的排序损失，如果负样本远离正样本并超出间隔，则容易导致零损失问题。 此外，实体在不同上下文中应具有不同的表示。 为解决这些问题，我们提出了一种新颖的关系感知网络与基于注意力的损失（RANA）框架。 具体而言，为了更好地利用丰富的负样本并缓解零损失问题，我们有策略地选择相关的负样本，并设计一个基于注意力的损失函数以进一步区分每个负样本的重要性。 其直觉是，与正样本更相似的负样本会对模型产生更大的贡献。 此外，我们设计了一个动态的关系感知实体编码器，用于学习上下文依赖的实体表示。 实验表明，RANA在两个基准数据集上优于最先进的模型。