  Recent years have seen significant advancements in multi-modal knowledge
graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by
integrating multi-modal entity information, thereby facilitating the discovery
of unobserved triples in the large-scale knowledge graphs (KGs). Nevertheless,
existing methods emphasize the design of elegant KGC models to facilitate
modality interaction, neglecting the real-life problem of missing modalities in
KGs. The missing modality information impedes modal interaction, consequently
undermining the model's performance. In this paper, we propose a modality
adversarial and contrastive framework (MACO) to solve the modality-missing
problem in MMKGC. MACO trains a generator and discriminator adversarially to
generate missing modality features that can be incorporated into the MMKGC
model. Meanwhile, we design a cross-modal contrastive loss to improve the
performance of the generator. Experiments on public benchmarks with further
explorations demonstrate that MACO could achieve state-of-the-art results and
serve as a versatile framework to bolster various MMKGC models. Our code and
benchmark data are available at https://github.com/zjukg/MACO.
