Multi-modal entity alignment (MMEA) identifies identical real-world entities across two multi-modal knowledge graphs. Most existing methods heavily rely on costly manually labeled seed alignments; in response, self-supervised MMEA has emerged to reduce this dependency. However, current self-supervised approaches suffer from two key issues: (1) low-quality pseudo-labels (sparse and noisy) weakening self-supervised signals; and (2) inter-modal semantic inconsistencies (structure, text, vision) hindering unified entity representations. To resolve these issues, we propose OPICE, an Ontology-guided Pseudo-label Generation and Inter-modal Consistency Enhancement for self-supervised MMEA. It adopts a robust pseudo-label generation strategy to produce more initial alignments with less noise, and it uses an inter-modal consistency enhancement module to narrow inter-modal semantic gaps for unified representations. Experiments on FB–DB15K and FB–YAGO15K show that OPICE achieves state-of-the-art performance, improving Hit@1 by 6.8% on average over the strongest self-supervised baseline and being competitive with most supervised baselines under standard reported settings.

Multimodal entity alignment identifies equivalent entities referring to the same real-world object across heterogeneous multimodal knowledge graphs and is essential for multi-source knowledge integration. Existing methods often rely on manually labeled seed alignments and multimodal feature similarity, limiting their ability to capture implicit semantic equivalence under heterogeneous attribute and relation expressions. Large language models (LLMs) offer strong semantic understanding and reasoning capabilities for unsupervised alignment, but their direct application remains challenging because of the large entity search space, redundant knowledge graph information, and the stochasticity of LLM inference. To address these challenges, we propose MEA-LLM, an LLM-based fully unsupervised multimodal semantic-enhanced entity alignment method. MEA-LLM first pre-screens candidate entities by jointly exploiting multimodal similarities derived from visual, attribute, and relational information. It then selects discriminative attributes and relations based on degree of freedom, candidate frequency, and discriminability for LLM reasoning. Multiple inference and result aggregation are further employed to improve robustness. Experiments on FB-DB15K and FB-YAGO15K show that MEA-LLM outperforms existing methods across multiple evaluation metrics. Further ablation studies, hyperparameter analyses, experiments with different model-scales, and efficiency evaluations demonstrate the effectiveness of the core components and indicate that MEA-LLM achieves a favorable trade-off between entity alignment performance and inference cost.