RAPC: Relation Answer Space Prototype Calibration for Multimodal Knowledge Graph Completion

Abstract

Multimodal knowledge graph completion (MMKGC) aims to predict missing entities by exploiting structural, visual, and textual information and is important for semantic retrieval, recommendation, and intelligent question answering. Existing relation-aware methods usually use relational context to adjust modality weights or fuse multimodal scores but rarely exploit the historical answer distribution of each directed relation as explicit ranking evidence. To address this limitation, we propose RAPC, a Relation Answer Space Prototype Calibration framework. For each query, RAPC obtains multimodal prior scores from structural, visual, textual, and image–text cross-modal branches, retrieves the top-k query-relevant anchors from the historical answer space of the corresponding directed relation, and aggregates them into prototype evidence. This evidence is injected through a prior-preserving selective calibration mechanism, while relation-aware hard negative training improves discrimination between true answers and similar false candidates. Experiments on DB15K, MKG-W, and MKG-Y show that RAPC achieves clear gains on DB15K and MKG-W and obtains the best MRR and Hits@1 on MKG-Y. Compared with the best external results, RAPC improves MRR and Hits@1 by 2.64 and 3.57 percentage points on DB15K and by 3.02 and 3.72 percentage points on MKG-W. These results show that relation-level historical answer distributions provide useful explicit evidence for top-ranked entity prediction.