Graph convolutional networks (GCNs) -- which are effective in modeling graph
structures -- have been increasingly popular in knowledge graph completion
(KGC). GCN-based KGC models first use GCNs to generate expressive entity
representations and then use knowledge graph embedding (KGE) models to capture
the interactions among entities and relations. However, many GCN-based KGC
models fail to outperform state-of-the-art KGE models though introducing
additional computational complexity. This phenomenon motivates us to explore
the real effect of GCNs in KGC. Therefore, in this paper, we build upon
representative GCN-based KGC models and introduce variants to find which factor
of GCNs is critical in KGC. Surprisingly, we observe from experiments that the
graph structure modeling in GCNs does not have a significant impact on the
performance of KGC models, which is in contrast to the common belief. Instead,
the transformations for entity representations are responsible for the
performance improvements. Based on the observation, we propose a simple yet
effective framework named LTE-KGE, which equips existing KGE models with
linearly transformed entity embeddings. Experiments demonstrate that LTE-KGE
models lead to similar performance improvements with GCN-based KGC methods,
while being more computationally efficient. These results suggest that existing
GCNs are unnecessary for KGC, and novel GCN-based KGC models should count on
more ablation studies to validate their effectiveness. The code of all the
experiments is available on GitHub at this https URL.

本文通过建立变体研究了基于 Graph Convolutional Networks 的知识图谱完成模型，在实验中发现了与预期相反的图结构建模并不对模型性能产生显著影响，而实体表示的转换是改善性能的关键因素。作者基于此提出了 LTE-KGE 框架，通过线性转换实体嵌入向现有的 KGE 模型中添加 GCNs 模块以达到类似的性能提升。实验结果表明，与 GCN-based KGC 方法相比，LTE-KGE 模型在性能上有类似的提升，同时更具计算效率。因此，本文表明 GCNs 对于 KGC 并不必要，新型基于 GCNs 的 KGC 模型需要进行更多的消融研究来验证其有效性。