  Knowledge graphs (KGs) that modelings the world knowledge as structural
triples are inevitably incomplete. Such problems still exist for multimodal
knowledge graphs (MMKGs). Thus, knowledge graph completion (KGC) is of great
importance to predict the missing triples in the existing KGs. As for the
existing KGC methods, embedding-based methods rely on manual design to leverage
multimodal information while finetune-based approaches are not superior to
embedding-based methods in link prediction. To address these problems, we
propose a VisualBERT-enhanced Knowledge Graph Completion model (VBKGC for
short). VBKGC could capture deeply fused multimodal information for entities
and integrate them into the KGC model. Besides, we achieve the co-design of the
KGC model and negative sampling by designing a new negative sampling strategy
called twins negative sampling. Twins negative sampling is suitable for
multimodal scenarios and could align different embeddings for entities. We
conduct extensive experiments to show the outstanding performance of VBKGC on
the link prediction task and make further exploration of VBKGC.
