## 1 Introduction
This is crucial in real-world EA scenarios since knowledge graphs (especially MMKG) discovered from the internet or professional domains inevitably contain errors and noise, such as those with unrecognized images. Additionally, intra-modal feature differences and inter-modal phenomena such as modality absence, imbalance, or ambiguity are common in KGs. These shortcomings affect their robustness to some extent. Through research, it was found that the current entity alignment methods have the following three issues:  
1. The existing entity alignment methods focus more on the entity alignment of traditional textual knowledge graphs. Some research embeds knowledge graphs from different sources into a low-dimensional space and achieves entity alignment by calculating the similarity between entities, yielding good results. However, these methods only utilize single-modal data (text) and ignore other modal data (images), thus failing to fully exploit the entity feature information in other modal data. 2. Traditional cross-modal entity alignment methods often require extensive manual data annotation or carefully designed alignment features. For example, [PERSON] [36] proposed an adaptive co-attention network that selected Twitter as the data source, crawled and annotated a dataset containing images, and controlled the preference level of each word for the images and text using gate and filter mechanisms. While these traditional entity alignment methods can achieve high alignment effectiveness, they require a considerable amount of manual annotation, resulting in time wastage and increased labor costs. Moreover, the entity features designed by such methods often lack scalability and universality. 3. Multimodal pre-trained language models achieve cross-modal entity alignment by pre-training on a large amount of unlabeled data. However, this method mostly focuses on global image and text features and is designed only for English text-image pairs. Models like CLIP pre-trained language models do not model the fine-grained relationships between text and images, which are valuable in domain-specific multimodal knowledge graph cross-modal EA tasks. Additionally, image-text pairs often contain noise in practice. Based on these issues, this paper proposes a knowledge graph entity alignment method based on multimodal data supervised (MDSEA). It first uses Transformer to obtain knowledge graph entity encoding representations. Then, it employs a multimodal supervised method for knowledge graph entity representation learning, ensuring that the vector representation of the entities contains rich multimodal semantic information, enhancing the generalization ability of the learned entity representation. Finally, it maps information from different modalities to a shared low-dimensional subspace, making similar entities closer in the subspace, thus optimizing the effect of the entity alignment. The main contributions are as follows:  
1. An embedding-based cross-lingual entity alignment method was proposed that uses Transformer to obtain knowledge graph entity encoding representations. Under multimodal information supervision, different models of information are mapped to a shared low-dimensional subspace to achieve entity alignment. 2. We proposed a multimodal supervised strategy for knowledge graph entity representation learning, ensuring that the vector representation of the entities contains rich multimodal semantic information, enhancing the generalization ability of the learned entity representation. 3. We evaluated the proposed method on a real cross-lingual dataset from DBpedia. The experimental results showed that the proposed method outperforms several cross-lingual entity alignment methods on Hits@1, Hits@10, and MRR. The framework is simple, fast, and has strong interpretability.