Multimodal knowledge graphs (MMKG) store structured world knowledge enriched with multimodal descriptive information. However, MMKG often faces the challenge of incompleteness. The primary objective of multimodal knowledge graph completion (MMKGC) is to predict missing entities within MMKG. Current MMKGC methods struggle with addressing the issue of over-trust attention and how to enhance the robustness of the model. To overcome these problems, we introduce APKGC, a noise-enhanced multimodal method for knowledge graph completion with attention penalty. APKGC effectively adjusts the attention scores in the language model and alleviates over-trust attention through a specifically designed attention penalty module. Additionally, an adaptive noise sampling module is proposed to supplement the entity's multimodal information, thereby enhancing the model's robustness. Experimental evaluation demonstrates that APKGC excels in overcoming these challenges. Compared to the existing state-of-the-art MMKGC model, APKGC improves Hit@1 by 3.3% on the DB15K dataset and by 3.4% on the MKG-W dataset.

Knowledge graphs (KGs) are frequently confronted with the challenge of incompleteness, a problem that extends to multimodal knowledge graphs (MKGs). The primary goal of multimodal knowledge graph completion (MKGC) is to predict missing entities within MKGs. However, current MKGC methods face difficulties in adequately addressing modal preferences and imbalances in modal information. To overcome these issues, we introduce AdaMKGC, an innovative hybrid model incorporating an adaptive modality interaction transformer. This model employs a dynamic attention interaction strategy and a self-enhancing sampling approach. AdaMKGC achieves a more precise utilization of multimodal information by integrating modal preference information into modal interactions. Additionally, it effectively mitigates the issue of modal imbalance through targeted sampling and adjustment for entities with deficient information. Experimental evaluations demonstrate AdaMKGC’s superior performance in overcoming these prevalent challenges. Compared to existing state-of-the-art MKGC models, AdaMKGC shows a notable enhancement of 28% in MR on the WN18-IMG dataset and an improvement of 2.7% in Hits@1 on the FB15k-237-IMG dataset. Our code is available at https://github.com/HubuKG/AdaMKGC .