# Multi-Modal Entity Alignment Method Based on Feature Enhancement  
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task performance based on the knowledge graph. Entity alignment aims to use models and algorithms to determine whether two entities with different sources and representations refer to the same object in reality, thus reducing the number of redundant entities in the knowledge graph and integrating the triple information of aligned entities. Therefore, entity alignment is an important supporting technology for improving multi-modal knowledge graphs. We denote two pre-aligned multi-modal knowledge graphs as \(G_{1}=(E_{1},R_{1},A_{1},V_{1},T_{1})\) and \(G_{2}=(E_{2},R_{2},A_{2},V_{2},T_{2})\), where \(E_{i},R_{i},A_{i},V_{i},T_{i},i\in\{1,2\}\) are the sets of entities, relations, attributes, visual information, and triples, respectively, of two graphs. Multi-modal entity alignment aims to find aligned entity pairs \(S=\{(e_{1},e_{2})\in E_{1}\times E_{2}|e_{1}\Leftrightarrow e_{2}\}\). The symbol \(\Leftrightarrow\) means that the entity \(e_{1}\) from \(E_{1}\) and the entity \(e_{2}\) from \(E_{2}\) have the same semantic; \(e_{1}\) and \(e_{2}\) refer to the same object in reality. Some researchers have proposed effective models in the field of multi-modal entity alignment. For example, MMEA [7] models different modal information of entity attributes and conducts knowledge fusion to achieve the effect of modeling and alignment of multi-modal entities. EVA [8] uses the visual similarity of entities to create an initial seed dictionary, providing a completely unsupervised solution. HMEA [9] models multi-modal representations in the hyperbolic space to improve the entity representation capability. MSNEA [10] also extracts visual, relational, and attribute features of entities separately, integrates visual features based on modal enhancement mechanisms to guide multi-modal feature learning, and adaptively allocates attention weights to capture valuable attributes for alignment. MCLEA [11] jointly models the intra-modality and inter-modality interactions based on contrastive learning after acquiring the attribute features of each modality to improve the model's representation capability. Table 1 shows the comparison of main stream multi-modal entity alignment models. However, the previously mentioned models did not fully utilize the entity information of each modality but only relied on the pre-trained encoders for encoding. This limits their ability to extract deep features from the data. For instance, the equivalent entities that refer to the same mo  
Figure 1: An example of a multi-modal knowledge graph. have different description images (such as different movie posters) in various knowledge graphs, which may result in low similarity if only a pre-trained visual model is used for encoding. However, there may be similar text in the posters, such as the movie name and slogan. By extracting the text information to supplement the entity image information, the alignment accuracy can be further improved. To further enhance the utilization of entity multi-modal information, this paper proposes **MEAFE**: a multi-modal entity alignment method based on feature enhancement. The core idea is to maximize the use of entity visual, textual, and relational modals to enhance the corresponding feature embedding and improve the knowledge representation ability of the model. Firstly, MEAFE enhances the image modality information by cleaning up the low semantic relevant images using a pre-trained multi-modal model, extracting text information possibly present in the entity image using an OCR (Optical Character Recognition) model, and then encodes it with a pre-trained language model as a supplementary representation of the image embedding. Secondly, the GATv2 [12] network is used instead of traditional graph convolutional networks [13] or graph attention networks to extract the neighborhood structure features of the entity.