Continual Completion for Dynamic Multimodal Knowledge Graphs via Semantic Alignment and Adaptive Replay Enhancement

To meet the demand for dynamic knowledge updating and long-term knowledge retention in knowledge-enhanced artificial intelligence, dynamic multimodal knowledge graph continual completion infers missing facts incrementally as graph snapshots evolve while keeping historical knowledge stable. It thus provides fundamental support for dynamic knowledge updates. Existing methods predominantly focus on single-modal structural information, failing to fully exploit textual and visual semantics; directly incorporating multimodal information may introduce unexpected semantic shifts across snapshots, thereby exacerbating catastrophic forgetting. To this end, this paper proposes SAREC, a semantic alignment and adaptive replay enhanced method for dynamic multimodal knowledge graph continual completion. SAREC leverages large language models to generate entity semantic descriptions and combines them with vision-language pretrained representations to construct a multimodal discrepancy-aware fusion mechanism, enhancing the completeness and robustness of entity representations. A multimodal temporal semantic alignment module is further introduced to constrain cross-snapshot representation evolution and maintain semantic consistency. Finally, a temporal-aware adaptive replay mechanism is proposed, which dynamically allocates the replay budget according to snapshot recency and selectively replays historical knowledge to alleviate catastrophic forgetting. Experiments demonstrate that SAREC achieves superior performance under various knowledge evolution modes, providing effective support for continual knowledge injection and adaptive knowledge enhancement in open dynamic environments.