Abstract
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize intrinsic relation rules to infer missing links. However, this paper points out that the multi-hop relation rules are hard to be reliably memorized due to the inherent deficiencies of such implicit memorization strategy, making embedding models underperform in predicting links between distant entity pairs. To alleviate this problem, we present Vertical Learning Paradigm (VLP), which extends embedding models by allowing to explicitly copy target information from related factual triples for more accurate prediction. Rather than solely relying on the implicit memory, VLP directly provides additional cues to improve the generalization ability of embedding models, especially making the distant link prediction significantly easier. Moreover, we also propose a novel relative distance based negative sampling technique (ReD) for more effective optimization. Experiments demonstrate the validity and generality of our proposals on two standard benchmarks. Our code is available at this https URL.
Abstract (translated)
嵌入模型在知识图的完成任务(KGC)中展现出了巨大的能力。通过学习每个训练三元组的结构约束，这些方法会潜意识地记住内在的关系规则来推断缺失的链接。然而，本文指出，多级关系规则很难可靠地记忆，因为这种类型的隐含记忆策略固有的缺陷，导致嵌入模型在预测距离实体 pairs 的链接时表现不佳。为了解决这一问题，我们提出了垂直学习范式(VLP)，它扩展了嵌入模型，允许从相关事实三元组中 explicitly 复制目标信息，以更准确地预测链接。而不仅仅是依赖隐含记忆，VLP 直接提供了额外的提示，以提高嵌入模型的泛化能力，特别是使远程链接预测变得更容易。此外，我们还提出了一种新颖的相对距离基于负采样技术(ReD)，以更有效的优化。实验证明了我们提出的建议的两个标准基准的 validity 和 generalizability。我们的代码现在可以在这个 https URL 上可用。
URL
https://arxiv.org/abs/2305.14126
