In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic inconsistency, mainly due to missing modal attributes, poses a significant challenge. Traditional approaches rely on attribute interpolation, but this often introduces modality noise, distorting the original semantics. Moreover, the lack of a universal theoretical framework limits advancements in achieving semantic consistency. This study introduces a novel approach, DESAlign, which addresses these issues by applying a theoretical framework based on Dirichlet energy to ensure semantic consistency. We discover that semantic inconsistency leads to model overfitting to modality noise, causing performance fluctuations, particularly when modalities are missing. DESAlign innovatively combats over-smoothing and interpolates absent semantics using existing modalities. Our approach includes a multi-modal knowledge graph learning strategy and a propagation technique that employs existing semantic features to compensate for missing ones, providing explicit Euler solutions. Comprehensive evaluations across 18 benchmarks, including monolingual and bilingual scenarios, demonstrate that DESAlign surpasses existing methods, setting a new standard in performance. Further testing on 42 benchmarks with high rates of missing modalities confirms its robustness, offering an effective solution to semantic inconsistency in real-world MMKGs.

翻译：在多模态知识图谱（MMKGs）中，多模态实体对齐（MMEA）对于跨不同模态属性识别相同实体至关重要。然而，主要由模态属性缺失导致的语义不一致性构成了重大挑战。传统方法依赖属性插值，但这常引入模态噪声，扭曲原始语义。此外，缺乏统一理论框架限制了语义一致性方面的进展。本研究提出了一种名为DESAlign的新方法，通过应用基于狄利克雷能量的理论框架确保语义一致性，从而解决这些问题。我们发现，语义不一致性会导致模型过拟合模态噪声，引起性能波动，尤其在模态缺失时。DESAlign创新性地缓解了过度平滑问题，并利用现有模态插补缺失语义。我们的方法包括一种多模态知识图谱学习策略和一种传播技术，该技术利用现有语义特征补偿缺失特征，并提供显式欧拉解。跨18个基准（包括单语和双语场景）的综合评估表明，DESAlign超越了现有方法，树立了新的性能标准。在42个具有高模态缺失率的基准上的进一步测试证实了其鲁棒性，为实际MMKGs中的语义不一致性提供了有效解决方案。