Huayu Li1,2, Xinxin Chen1,2, Lizhuang Tan3,4,*, Konstantin I. Kostromitin5,6, Athanasios V. Vasilakos7, Peiying Zhang1,2
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4133-4153, 2025, DOI:10.32604/cmc.2025.069690
- 23 September 2025
Abstract To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising from modal heterogeneity during fusion, while also capturing shared information across modalities, this paper proposes a Multi-modal Pre-synergistic Entity Alignment model based on Cross-modal Mutual Information Strategy Optimization (MPSEA). The model first employs independent encoders to process multi-modal features, including text, images, and numerical values. Next, a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information. This pre-fusion strategy enables unified perception of heterogeneous modalities at the More >
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