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 .

Electroencephalogram (EEG) is a signal commonly used for detecting brain activity and diagnosing sleep disorders. Manual sleep stage scoring is a time-consuming task, and extracting information from the EEG signal is difficult because of the non-linear dependencies of time series. To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time and frequency domains was realized using a convolutional neural network to expand the scope of feature extraction and preserve the original EEG feature information to the maximum extent possible. Finally, the time-series information was mined using the encoding–decoding module of the BiRNN, and the automatic discrimination of the sleep staging of the EEG signal was realized using the SoftMax function. The model was cross-validated using Fpz-Cz single-channel EEG signals from the Sleep-EDF dataset for 19 nights, and the results demonstrated that the proposed model can achieve a high recognition rate and stability.

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.

Inductive reasoning aims to infer missing knowledge for unseen entities and relations. Existing methods exhibit limited generalization capabilities due to their dependence on localized structural patterns and inadequate handling of graph imbalance. To address these challenges, we propose a novel Hi erarchical Mod eling with Graph Perturbation-Enhanced Network (HiMod), which effectively integrates hierarchical relation modeling with a dynamic perturbation mechanism to enhance the generalization ability of inductive reasoning models. HiMod leverages a hierarchical relation modeling mechanism that maps specific relations to higher-level general concepts within a global semantic framework. This allows for capturing semantic commonalities across relations, enabling robust reasoning for unseen queries. Simultaneously, a dynamic perturbation mechanism is introduced to adjust perturbation strength based on node importance and graph sparsity, facilitating deeper exploration of the latent semantic space and mitigating the effects of graph imbalance. Extensive experiments on three benchmark inductive knowledge graph reasoning datasets demonstrate that HiMod achieves the most significant MRR improvements among the four split versions, with 11.17% on WN18RR, 4.61% on FB15K-237, and 5.47% on NELL-995. Our code is available at https://github.com/HubuKG/HiMod .