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.

翻译：近年来，多模态知识图谱补全（MMKGC）取得了显著进展。MMKGC通过整合多模态实体信息来增强知识图谱补全（KGC），从而有助于在大规模知识图谱（KGs）中发现未观测到的三元组。然而，现有方法侧重于设计优雅的KGC模型以促进模态交互，忽略了KGs中模态缺失的现实问题。缺失的模态信息阻碍了模态交互，进而损害了模型性能。在本文中，我们提出了一种模态对抗与对比框架（MACO）以解决MMKGC中的模态缺失问题。MACO对抗性地训练生成器和判别器，生成可纳入MMKGC模型的缺失模态特征。同时，我们设计了一种跨模态对比损失来提升生成器的性能。在公开基准上的实验及进一步探索表明，MACO能够实现最先进的结果，并作为一个通用框架来增强各种MMKGC模型。我们的代码和基准数据可在 https://github.com/zjukg/MACO 获取。