ALMEA 框架解决多模态实体对齐中的抑制与高估问题
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment
多模态知识图谱对齐的新论文，指出现有方法把缺模态和图结构差异分开处理有漏洞，ALMEA 在 20% 监督下 MRR 涨了 5 个点以上，做 KG 融合的可以看。
arXiv 论文提出多模态实体对齐（MMEA）中两种新失败模式：Detail Missing 引发的高估和 Context Imbalance 引发的抑制。作者开发 ALMEA 框架，通过 CMI 补全缺失模态、LSC 校准跨图嵌入，并用 CIS 在稀疏监督下自适应获取伪标签。在 20% 监督率下，ALMEA 的 MRR 最高提升 5.20%，Hits@1 提升 6.40%。
Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment
Multimodal entity alignment (MMEA) is fundamental to integrating heterogeneous knowledge graphs (KGs), yet existing paradigms remain critically vulnerable to incomplete modalities and cross-graph structural asymmetries. This paper exposes a fundamental oversight in current MMEA research: treating feature missingness and topological discrepancies as isolated preprocessing or denoising challenges, which overlooks their destructive coupling during multimodal fusion. We systematically formulate and evaluate two newly identified failure modes: 1) Detail Missing (DM)-induced overvaluation, where the absence of local modality details causes models to overemphasize uninformative, blurred representations; and 2) Context Imbalance (CI)-induced suppression, where topological discrepancies across KGs allow dense structural contexts to overwhelm weaker but highly discriminative modal signals, triggering high-level semantic drift. To break this cycle of degradation, we introduce ALMEA, a generative and calibration framework that establishes a bidirectional feedback loop between low-level feature reconstruction and high-level latent space alignment. Specifically, Cross-Modality Imputation (CMI) synthesizes missing modality details to mitigate overvaluation, while Latent Semantic Calibration (LSC) aligns cross-graph joint embeddings to suppress topology-driven semantic drift. Under sparse supervision, we develop Controlled Iterative Selection (CIS) to adaptively acquire representative and diverse pseudo-labeled pairs, optimizing latent calibration under constrained budgets. Extensive evaluations on standard benchmarks demonstrate that ALMEA consistently outperforms state-of-the-art baselines. At a 20% supervision rate, ALMEA yields substantial absolute improvements of up to 5.20% in MRR and 6.40% in Hits@1, maintaining robust gains across higher supervision thresholds.
