Abstract
Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities. In this paper, we propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges. Firstly, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules. And we assign soft labels to these neighbors by solving a rule-constrained problem, rather than simply regarding them as unquestionably true. Secondly, many existing methods only use one-hop or two-hop neighbors for aggregation and ignore the distant information that may be helpful. Instead, we identify both logic and symmetric path rules to capture complex patterns. Finally, instead of one-time injection of rules, we employ an iterative learning scheme between the embedding method and virtual neighbor prediction to capture the interactions within. Experimental results on two knowledge graph completion tasks demonstrate that our VN network significantly outperforms state-of-the-art baselines. Furthermore, results on Subject/Object-R show that our proposed VN network is highly robust to the neighbor sparsity problem.
Abstract (translated)
将实体和关系嵌入到连续的向量空间近年来引起了浓厚兴趣。大多数嵌入方法都假定在训练过程中有所有的测试实体可用，这使得为新兴实体重新训练嵌入变得耗时。为解决这个问题，最近的工作在现有实体周围的图神经网络应用。在本文中，我们提出了一个新框架，即Virtual Neighbor（VN）网络，来解决三个关键挑战。首先，为了减少邻居稀疏问题，我们引入了基于规则的虚拟邻居的概念。然后，我们通过求解规则约束问题为这些邻居分配软标签，而不是简单地将它们视为无疑正确的。其次，许多现有方法仅使用一跳或两跳邻居进行聚合，并忽略了可能有助于发现的远处信息。相反，我们识别了逻辑和对称路径规则以捕捉复杂模式。最后，我们采用编码方法和虚拟邻居预测之间的迭代学习方案，而不是一次性将规则注入。在两个知识图补全任务上的实验结果表明，我们的VN网络在性能上明显优于最先进的基线。此外，在主题/对象R上的结果表明，我们提出的VN网络对于邻居稀疏问题具有高度的鲁棒性。
URL
https://arxiv.org/abs/2402.14033
