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 https://github.com/rui9812/VLP.

翻译：嵌入模型在知识图谱补全任务中展现出强大能力。通过对每个训练三元组学习结构约束，这类方法隐式记忆内在关系规则以推断缺失链接。然而，本文指出由于这种隐式记忆策略的固有缺陷，多跳关系规则难以被可靠记忆，导致嵌入模型在预测远距离实体对之间的链接时表现欠佳。为缓解该问题，我们提出纵向学习范式（VLP），通过允许从相关事实三元组中显式复制目标信息来扩展嵌入模型，从而实现更精准的预测。VLP不依赖隐式记忆，而是直接提供额外线索以提升嵌入模型的泛化能力，尤其显著简化了远距离链接预测任务。此外，我们提出基于相对距离的负采样技术（ReD），用于更高效的优化。实验在两个标准基准上验证了本研究方法的有效性与通用性。我们的代码开源于 https://github.com/rui9812/VLP。