  Knowledge graph completion (KGC) is the task of inferencing missing facts
from any given knowledge graphs (KG). Previous KGC methods typically represent
knowledge graph entities and relations as trainable continuous embeddings and
fuse the embeddings of the entity $h$ (or $t$) and relation $r$ into hidden
representations of query $(h, r, ?)$ (or $(?, r, t$)) to approximate the
missing entities. To achieve this, they either use shallow linear
transformations or deep convolutional modules. However, the linear
transformations suffer from the expressiveness issue while the deep
convolutional modules introduce unnecessary inductive bias, which could
potentially degrade the model performance. Thus, we propose a novel
Transformer-based Patch Refinement Model (PatReFormer) for KGC. PatReFormer
first segments the embedding into a sequence of patches and then employs
cross-attention modules to allow bi-directional embedding feature interaction
between the entities and relations, leading to a better understanding of the
underlying KG. We conduct experiments on four popular KGC benchmarks, WN18RR,
FB15k-237, YAGO37 and DB100K. The experimental results show significant
performance improvement from existing KGC methods on standard KGC evaluation
metrics, e.g., MRR and H@n. Our analysis first verifies the effectiveness of
our model design choices in PatReFormer. We then find that PatReFormer can
better capture KG information from a large relation embedding dimension.
Finally, we demonstrate that the strength of PatReFormer is at complex relation
types, compared to other KGC models
