When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs
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. Specifical...
| Authors | Linyu Li, Zhi Jin, Yichi Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 April 2026 |
| Last updated at source | 5 August 2026 |
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