Multi-modal knowledge graph completion (MMKGC) aims to automatically discover
new knowledge triples in the given multi-modal knowledge graphs (MMKGs), which
is achieved by collaborative modeling the structural information concealed in
massive triples and the multi-modal features of the entities. Existing methods
tend to focus on crafting elegant entity-wise multi-modal fusion strategies,
yet they overlook the utilization of multi-perspective features concealed
within the modalities under diverse relational contexts. To address this issue,
we introduce a novel MMKGC framework with Mixture of Modality Knowledge experts
(MoMoK for short) to learn adaptive multi-modal embedding under intricate
relational contexts. We design relation-guided modality knowledge experts to
acquire relation-aware modality embeddings and integrate the predictions from
multi-modalities to achieve comprehensive decisions. Additionally, we
disentangle the experts by minimizing their mutual information. Experiments on
four public MMKG benchmarks demonstrate the outstanding performance of MoMoK
under complex scenarios.

多模态知识图谱补全 (MMKGC) 旨在通过协同建模大量三元组中隐藏的结构信息和实体的多模态特征，自动发现给定的多模态知识图谱中的新知识三元组。我们引入了一种新颖的 MMKGC 框架，即混合模态知识专家 (MoMoK)，来学习在复杂关系环境下自适应的多模态嵌入。通过设计关系指导的模态知识专家来获取关系感知的模态嵌入，并整合多模态的预测结果以实现全面的决策。此外，通过最小化它们的互信息来解开这些专家之间的联系。在四个公开的 MMKG 基准上的实验证明了 MoMoK 在复杂情境下的出色性能。