# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
###### Abstract  
Entity alignment helps discover and link entities from different knowledge graphs (KGs) that refer to the same real-world entity, making it a critical technique for KG fusion. Most entity alignment methods are based on knowledge representation learning, which uses a mapping function to project entities from different KGs into a unified vector space and align them based on calculated similarities. However, this process requires sufficient pre-aligned entity pairs. To address this problem, this study proposes an entity alignment method based on joint learning of entity and attribute representations. Structural embeddings are learned using the triples modeling method based on TransE and PTransE and extracted from the embedding vector space utilizing semantic information from direct and multi-step relation paths. Simultaneously, attribute character embeddings are learned using the N-gram-based compositional function to encode a character sequence for the attribute values, followed by TransE to model attribute triples in the embedding vector space to obtain attribute character embedding vectors. By learning the structural and attribute character embeddings simultaneously, the structural embeddings of entities from different KGs can be transferred into a unified vector space. Lastly, the similarities in the structural embedding of different entities were calculated to perform entity alignment. The experimental results showed that the proposed method performed well on the DBP15K and DWK100K datasets, and it outperformed currently available entity alignment methods by 16.8, 27.  
that refer to the same real-world entity. As each KG is developed independently, different KGs will have different knowledge sources, resulting in different surface forms, posing a significant problem when fusing different KGs. Therefore, knowledge source fusion, integration of existing knowledge resources, and large-scale knowledge mapping are crucial endeavors. Entity alignment can be used to link knowledge from different KGs and fuse KGs into larger, authoritative domain KGs, which can serve as a knowledge base for downstream applications. However, entity alignment requires complex calculations, and the conventional approach involves using entity-based labeling and handcrafted features, both of which are laborious and difficult to scale in terms of practical applications. Translation-based representation learning is a method for learning vector representations for entities in a dense low-dimensional space. TransE [9], a classic example of this approach, is the first method to utilize the structural embeddings of entities in a KG. Extensions of TransE, such as TransR [10], TransH [11], and TransD [12], are used to learn entity embedding representations and perform entity alignment or entity inference in the unified vector space, where the likelihood of alignment between any pair of entities is proportional to their semantic similarity, i.e., closeness in the embedding space. The most recent advances in KG entity alignment are embedding-based techniques, such as MTransE [13]--a translation-based model for multilingual KG embeddings in which existing multilingual KGs are used for cross-lingual entity alignment. Herein, the entities and relations of each language are encoded in separated embedding spaces, whereas an alignment model embeds the multilingual vectors into an independent space where they are aligned by translation and linear transformations. MTRansE aims to facilitate the construction of a coherent multilingual knowledge base and assist machines in dealing with expressions of entity relationships across different human languages. In IPTransE [14], entity alignment is performed using an iterative- and parameter-sharing method for joint knowledge embeddings. As this method requires manual labeling to produce a seed set of pre-aligned entities, whose structure is projected to a low-dimensional vector space, it is highly dependent on the accuracy of the manually labeled seed set.