Abstract
Real-world multimodal knowledge graphs (MMKGs) are dynamic, with new entities, relations, and multimodal knowledge emerging over time. Existing continual knowledge graph reasoning (CKGR) methods focus on structural triples and cannot fully exploit multimodal signals from new entities. Existing multimodal knowledge graph reasoning (MMKGR) methods, however, usually assume static graphs and suffer catastrophic forgetting as graphs evolve. To address this gap, we present a systematic study of continual multimodal knowledge graph reasoning (CMMKGR). We construct several continual multimodal knowledge graph benchmarks from existing MMKG datasets and propose MRCKG, a new CMMKGR model. Specifically, MRCKG employs a multimodal-structural collaborative curriculum to schedule progressive learning based on the structural connectivity of new triples to the historical graph and their multimodal compatibility. It also introduces a cross-modal knowledge preservation mechanism to mitigate forgetting through entity representation stability, relational semantic consistency, and modality anchoring. In addition, a multimodal contrastive replay scheme with a two-stage optimization strategy reinforces learned knowledge via multimodal importance sampling and representation alignment. Experiments on multiple datasets show that MRCKG preserves previously learned multimodal knowledge while substantially improving the learning of new knowledge.
Abstract (translated)
现实世界中的多模态知识图谱（MMKGs）具有动态性，新实体、关系及多模态知识会随时间不断涌现。现有持续知识图谱推理（CKGR）方法仅关注结构三元组，无法充分利用新实体的多模态信号；而现有多模态知识图谱推理（MMKGR）方法通常假设图谱静态，在动态演化中易出现灾难性遗忘。为填补这一空白，我们对持续多模态知识图谱推理（CMMKGR）进行了系统性研究：基于现有MMKG数据集构建了多个持续多模态知识图谱基准，并提出了新型CMMKGR模型MRCKG。具体而言，MRCKG采用多模态-结构协同课程机制，依据新三元组与历史图谱的结构关联性及其多模态兼容性规划渐进式学习；引入跨模态知识保持机制，通过实体表征稳定性、关系语义一致性与模态锚定缓解遗忘问题；此外，设计多模态对比回放方案与两阶段优化策略，借助多模态重要性采样与表征对齐强化已学知识。多数据集实验表明，MRCKG在高效学习新知识的同时，能有效保持已掌握的多模态知识。
URL
https://arxiv.org/abs/2604.02778
