# Knowledge Graph Alignment Network with Node-Level Strong Fusion
###### Abstract  
Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then structure information is learned based on these two aspects of information through graph convolutional network (GCN), the alignment signals from attribute and name are already carried at the beginning of the learning structure. In the process of continuous propagation of multi-hop neighborhoods, the effect of strong fusion of structure, attribute and name information is achieved and the more meticulous entity representations are obtained. Additionally, through the continuous interaction between sub-alignment tasks, the effect of entity alignment is enhanced. An iterative framework is designed to improve performance while reducing the impact on pre-aligned seed pairs. Furthermore, extensive experiments demonstrate that the model improves the accuracy of entity alignment and significantly outperforms 13 previous state-of-the-art methods. knowledge graph; entity alignment; graph convolutional network; knowledge fusion Article  
Knowledge Graph Alignment Network with Node-Level Strong Fusion. _Appl. Sci._**2022**, _12_, 9434. [[https://doi.org/10.3390/app12199434](https://doi.org/10.3390/app12199434)]([https://doi.org/10.3390/app12199434](https://doi.org/10.3390/app12199434))  
Academic Editor: [PERSON]  
Received: 4 August 2022  
Accepted: 18 September 2022  
Published: 20 September 2022  
**Published:** **20 September 2022**  
**Published:** **2022**  
Convolutional Network (RDGCN) [10] and Neighborhood Matching Network (NMN) [11]. These methods only learn and improve the entity structure information and are lacking reflecting the entity meaning in all aspects. In the field of entity alignment, most previous research was based on the structure assumptions that entities with the same or similar neighborhoods are usually regarded as equivalent entities. These methods are judged by comparing adjacent entities. Figure 1 shows the English version of \"Oban\" and the Chinese version of \"Stonehaven\" as the central entity respectively. The central entity in Figure 2 is \"Steam Engine\" in English and \"England\" in Chinese. If only the alignment is judged by comparing neighborhoods, then these two pairs of entities will be incorrectly judged as aligned. However, as shown in Figure 1, if the attribute information \"Postal Code\" is added, which is unique attribute value label, it can correctly be determined that \"Oban\" and \"Stonehaven\" are non-equivalent entities. In Figure 2, considering the influence of relation information, it can be found that \"Steam Engine\" and \"England\" have different correspondences when facing the entities of \"[PERSON]\", \"Industrial Revolution\" and \"University of Glasgow\". In Figure 2, by contrast, it can be found that the relation between the two central entities \"Steam Engine\", \"England\" and their common neighbor entities \"[PERSON]\", \"Industrial Revolution\", \"University of Glasgow\" are different. For example, the \"steam engine\" was improved by \"[PERSON]\", but the relation between \"England\" and \"[PERSON]\" is the birthplace. The difference in the correspondence between them can be judged by the fact that these two entities with similar structure are not equivalent entities. Thence, attribute information and relation information can improve the accuracy of entity alignment. Therefore, this paper proposes a new embedded entity alignment model SFEA that integrates multi-faceted information with node-level strong fusion.