  As a crucial extension of entity alignment (EA), multi-modal entity alignment
(MMEA) aims to identify identical entities across disparate knowledge graphs
(KGs) by exploiting associated visual information. However, existing MMEA
approaches primarily concentrate on the fusion paradigm of multi-modal entity
features, while neglecting the challenges presented by the pervasive phenomenon
of missing and intrinsic ambiguity of visual images. In this paper, we present
a further analysis of visual modality incompleteness, benchmarking latest MMEA
models on our proposed dataset MMEA-UMVM, where the types of alignment KGs
covering bilingual and monolingual, with standard (non-iterative) and iterative
training paradigms to evaluate the model performance. Our research indicates
that, in the face of modality incompleteness, models succumb to overfitting the
modality noise, and exhibit performance oscillations or declines at high rates
of missing modality. This proves that the inclusion of additional multi-modal
data can sometimes adversely affect EA. To address these challenges, we
introduce UMAEA , a robust multi-modal entity alignment approach designed to
tackle uncertainly missing and ambiguous visual modalities. It consistently
achieves SOTA performance across all 97 benchmark splits, significantly
surpassing existing baselines with limited parameters and time consumption,
while effectively alleviating the identified limitations of other models. Our
code and benchmark data are available at https://github.com/zjukg/UMAEA.
