# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks  
Hongchan Li  
School of Computer and Communication Engineering, Zhengzhou University of Light Industry,  
Zhengzhou 450002, China; [EMAIL_ADDRESS]  
1  
Zhaoyang Han  
School of Computer and Communication Engineering, Zhengzhou University of Light Industry,  
Zhengzhou 450002, China; [EMAIL_ADDRESS]  
2  
Haodong Zhu  
School of Computer and Communication Engineering, Zhengzhou University of Light Industry,  
Zhengzhou 450002, China; [EMAIL_ADDRESS]  
1  
Yuchao Qian  
School of Computer and Communication Engineering, Zhengzhou University of Light Industry,  
Zhengzhou 450002, China; [EMAIL_ADDRESS]  
1  
Footnote 1: email: {z  
needs to be improved. Moreover, knowledge graphs are semi-structured data structures, and the accuracy of the traditional entity alignment techniques is limited, while heuristic algorithms are also difficult to generalize. With the emergence of Word2 Vec [3], the task of entity alignment has gradually been bifurcated into approaches that are based on translation and those that are based on graph neural networks [4] (GNNs). The fundamental concept behind both of these approaches is consistent. It involves acquiring an efficient vector representation of the KG within a low-dimensional space and subsequently executing entity alignment tasks based on the learned vector representation. This technique is collectively known as representation learning. A large number of experiments have evinced that translation-based methods are more suitable for link prediction, whereas GNNs excel at incorporating the neighborhood features of nodes. These capabilities can be harnessed to devise pertinent techniques for feature acquisition in entity alignment tasks, thereby enabling the attainment of superior accuracy and generalization capacity [5]. Although GNNs methods have achieved remarkable results, there are still three limitations. Firstly, most methods [6; 7; 8] regard KGs as homogeneous graphs and do not consider the heterogeneity of edges between different entities, whereas heterogeneous information can augment the accuracy and resilience of the model. Secondly, although many methods consider some semantic information beyond the relational structure, such as entity property information [9], entity description information [10], and entity name information [11], the more semantic information integrated into the methods, the more data are required, which is difficult to satisfy in many practical scenarios where the seed entities are often insufficient. Thirdly, some other works [12] only use the relational structures of different KGs to extract inter-graph information using graph matching networks (GMN) [13] to explore more analogous features between aligned entities. However, the introduction of the matching module throughout the training process results in an increase in the space and time complexity of the model, thereby impacting its efficiency. The proposed heterogeneous graph transformer with relation awareness (HGTRA) in this paper addresses the first limitation by effectively extracting similar features from aligned entities within their respective heterogeneous structures. To address the aforementioned latter two limitations, a new embedding model is introduced. The model initially generates property embeddings from the knowledge graph's property information and then relocates the entity embeddings of two knowledge graphs to the same vector space by leveraging the property embeddings. Thus, the similarity of properties between two knowledge graphs is crucial for generating a unified embedding space, which is also a major challenge in knowledge graph alignment tasks. Utilizing property embeddings, the PCE-HGTRA model can reciprocally transform the entity embeddings of both knowledge graphs to the identical vector space, allowing the entity embeddings to capture the property similarity from both knowledge graphs.