To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge
  Graph Completion
Rui Li Xu Chen Chaozhuo Li Yanming Shen Jianan Zhao Yujing Wang Weihao Han Hao Sun Weiwei Deng Qi Zhang Xing Xie
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.