  A large number of studies have emerged for Multimodal Knowledge Graph
Completion (MKGC) to predict the missing links in MKGs. However, fewer studies
have been proposed to study the inductive MKGC (IMKGC) involving emerging
entities unseen during training. Existing inductive approaches focus on
learning textual entity representations, which neglect rich semantic
information in visual modality. Moreover, they focus on aggregating structural
neighbors from existing KGs, which of emerging entities are usually limited.
However, the semantic neighbors are decoupled from the topology linkage and
usually imply the true target entity. In this paper, we propose the IMKGC task
and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the
contrast brings the helpful semantic neighbors close, and then the memorize
supports semantic neighbor retrieval to enhance inference. Specifically, we
first propose a unified cross-modal contrastive learning to simultaneously
capture the textual-visual and textual-textual correlations of query-entity
pairs in a unified representation space. The contrastive learning increases the
similarity of positive query-entity pairs, therefore making the representations
of helpful semantic neighbors close. Then, we explicitly memorize the knowledge
representations to support the semantic neighbor retrieval. At test time, we
retrieve the nearest semantic neighbors and interpolate them to the
query-entity similarity distribution to augment the final prediction. Extensive
experiments validate the effectiveness of CMR on three inductive MKGC datasets.
Codes are available at https://github.com/OreOZhao/CMR.
