# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
###### Abstract
If the labeling is incorrect, the iteration of the seed entity set will be extremely noisy, thereby decreasing the alignment accuracy. The JAPE model [15] jointly embeds the relationship structure of two KGs into a unified vector space and uses attribute correlations to cluster entities. Unlike other models, JAPE embeds attributes while learning entity embeddings and relationships between different KGs in a unified embedding space and uses attribute correlations to optimize entity embeddings. In other words, it is a joint attribute-preserving embedding model for cross-lingual entity alignment. However, if the attributes are heterogeneous and the KGs are ambiguously correlated, the effectiveness of attribute embedding can be significantly reduced. BootEA [16] adds bootstrapping to JAPE to reduce error accumulation in iteratively grown seed sets. Furthermore, it treats entity alignment as a classification problem and maximizes alignment likelihood over all labeled and unlabeled entities based on KG embeddings while using \(\varepsilon\)-truncated uniform negative sampling to improve the alignment performance. Additionally, labeled entities are edited or removed to reduce error accumulation during iterations. MultiKE [17] divides KG characteristics into multiple subsets, or views and embeds each entity into specific views for complementary learning from multiple views, enhancing alignment accuracy. KDCoE [18] uses the co-training approach to iteratively train two embedding models: one model is trained on the KG structure; the other is trained on entity descriptions for refining entity embeddings. All embedding-based KG entity alignment methods utilize the TransE family of models to learn the structural embeddings of entities and relations. However, these models can only embed the entity and relations of a single KG into the unified vector space. As the entities in each KG are named using different naming schemes, their structural embeddings fall in different vector spaces, making it impossible to directly calculate the entity similarity, highlighting one of the limitations of entity alignment. The current solution to this problem is to design a mapping function that projects entity embeddings from different KGs into a unified vector space. This mapping function can only be designed if some number of pre-aligned entities are already present; however, these pre-aligned entities are collected manually, which can be problematic. Furthermore, it is difficult to determine whether the entity pairs are aligned accurately, considering that any inaccuracy can profoundly affect the entity alignment process. To address the above issues, this study proposes to learn structural embeddings in parallel with attribute character embeddings. First, we calculate the similarities in the relations and attributes of the semantic embeddings. Relationships and attributes with the same meaning are then renamed with the same names, with manual inspection and amendment, followed by the joint learning of structural embeddings and attribute character embeddings from the relation and attribute triples. Finally, the structural embeddings of the entities are transferred to a unified vector space, and entity alignment is performed by calculating the similarities of the structural embeddings. The contributions of this study are summarized as follows:  
1. We proposed a relation and attribute alignment method based on cosine similarity. The cosine similarities of the semantic embeddings of relations and attributes are calculated, followed by the manual inspection and amendment of the results. Relation-attribute alignment is then performed by renaming relations and attributes with the same meaning to the same name. 2. A learning model is proposed to learn the structural and attribute character embeddings from different KGs using their relation and attribute triples.