ReRoute: Beyond Static Representations in Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MMKGC) integrates structural, visual, and textual information to predict missing facts. Static candidate representations use fixed modality combinations, although evidence requirements vary across relations and head/tail roles. Missing modalities further limit the available evidence. We propose ReRoute to adapt entity representations to these requirements. Its relation-role-aware multimodal routing (RRMR) adjusts visual and textual enhancement components on both the query and candidate sides. These adjustments depend on the relation and head/tail role. Structure-aware missing-modality compensation (SMMC) aggregates available modality-specific enhancement components from one-hop neighbors in the training graph. RRMR then modulates these supplementary signals. Decomposable scoring evaluates all entities without explicitly constructing conditioned candidate representations for each query. Across four evaluation metrics on DB15K, MKG-W, and MKG-Y, ReRoute ranks first in 10 of the 12 comparisons and second in the remaining 2. On MKG-W and MKG-Y, ReRoute achieves relative MRR improvements of 5.02% and 6.93%, respectively, over the strongest baseline on each dataset.