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

本文提出了 Vertical Learning Paradigm (VLP) 来解决知识图谱补全任务中跨度较大的多跳联系规则被困难隐式记忆的问题，VLP 扩展了嵌入模型，通过显式地从相关事实三元组中复制目标信息来提供额外线索，提高了其广义能力，尤其是在远程链接预测方面表现出色，同时提出了一种新的相对距离负采样技术（ReD）以进行更有效的优化。