# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 4 Discussion  
Currently, entity alignment is generally performed based on knowledge representation learning, which requires an adequate number of pre-aligned entity pairs to be collected in advance. Considering these pre-aligned entity pairs must be manually collected, the collection process may be plagued by various difficulties. In addition, it is difficult to ensure that the pre-aligned entity pairs are aligned accurately, considering that any inaccuracy can negatively affect the subsequent entity alignment process. Therefore, to address these issues, this study proposed an entity alignment method based on the joint learning of entity and attribute representations. We proposed a method for relation and attribute alignment based on cosine similarity by calculating the cosine similarities between the semantic embeddings of the relations and attributes, followed by manual inspection and amendments. The relations and attributes were then aligned by renaming the relations and attributes with the same meaning to the same name. Then, we proposed a learning model for the structural embeddings and attribute character embeddings of relation and attribute triples from different KGs. The structural embedding learning was performed via a triple modeling method using TransE and PTransE, which incorporated the semantic information contained by direct and multi-step relation paths, thereby extracting the structural embedding vectors from the embedding vector space. The attribute character embeddings were learned using the N-gram-based compositional function to encode a character sequence for the attribute values, with TransE used to model attribute triples in the embedding vector space to obtain attribute character embedding vectors. Additionally, the structural embeddings and attribute character embeddings were jointly learned to enable transferring structural embeddings from different KGs into a unified vector space. Entity alignment was then performed by calculating the similarities of the structural embeddings of entities. Moreover, a limit-based loss function was proposed to replace the conventional margin-based ranking criterion for learning structural and attribute character embeddings, which ensured that positive and negative triples received significantly low and high scores, respectively, thereby preventing drifts during the projection of structural embeddings into the unified vector space. Finally, we experimentally demonstrated that the proposed entity alignment method performed well on the DBP15K and DWK100K datasets and outperformed all entity alignment methods of the same type by a maximum of 16.8, 27.5, and 24.0% in precision, recall, and F1 measure, respectively.  
Conceptualization, C.X.; methodology, C.X.; software, C.X.; validation, C.X., L.Z. and Z.Z.; writing--original draft preparation, C.X.; writing--review and editing, C.X., L.Z. and Z.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 91538201, in part by Taishan Scholar Project of Shandong Province under Grant ts201511020, and in part by Project supported by Chinese National Key Laboratory of Science and Technology on Information System Security under Grant 614211190404.  
The data supporting the reported results are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.