# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 1 Introduction  
A knowledge graph (KG) is an important tool for efficiently using visualization technology to describe knowledge resources that constructs a relational network that can be understood by both computers and humans. It is a structured data model that stores and represents knowledge in the form of a graph. It represents entities in the real world and the relationships between them in the form of nodes and edges, thereby organizing and connecting information. It is composed of many triples consisting of entities, relationships, and attributes. For example, the sentence \"[PERSON] was born in Germany, and his profession was physicist\" can be decomposed into two triples: Einstein-birthplace-Germany and Einstein-profession-physicist. Although knowledge graphs and semantic networks have similarities in knowledge representation, they have their own focuses and characteristics in terms of structure, application, and implementation. Knowledge graphs focus more on data integration and query. They contain rich entity attributes and specific fact triples and have a higher degree of structure. Semantic networks focus more on the representation and reasoning of knowledge. They mainly focus on concepts and relationships, focus less on the representation of attributes and specific facts, and are more used for association and reasoning between concepts. With the rapid development of artificial intelligence and big-data technology, knowledge graphs are gradually penetrating into various fields, such as knowledge reasoning [1], recommendation systems [2], and question-answering systems [3]. The graphs are often incomplete, as they are built using different data sources for different purposes. As theamount of data continues to grow, a single graph often cannot meet the needs of downstream tasks, and the problem of how to find equivalent entities in the knowledge graphs constructed from different data sources is the challenge answered by knowledge fusion. Entity alignment (EA) is a basic task in knowledge fusion. It is the process of identifying and connecting the same real-world entities represented in different data sources or knowledge graphs. Its purpose is to eliminate redundancy, integrate scattered information, and use complementary information between different knowledge graphs to improve data consistency and accuracy and achieve a unified large-scale knowledge graph so as to better support downstream applications driven by knowledge graphs. Figure 1 shows an example of entity alignment. The first step is to find two aligned entities and further screen their neighbors. The neighbor nodes connected by dotted lines represent aligned entities, and they point to the same thing in the real world. Graph structure, as a powerful data representation, is widely used in entity-alignment tasks to effectively capture rich relationships and semantic information between entities, thereby enabling entity matching and alignment across data sources. Specific techniques have evolved, from traditional knowledge graph embedding (KGE) models such as MTransE [4] and IPTransE [5] to later neural networks (NNs) such as the attention-based graph convolutional network (GCN) [6], HGCN [7], RDGCN [8], and VR-GCN [9]. However, despite the significant progress made by graph-structure approaches in entity-alignment tasks, a number of challenges and constraints remain. In particular, when dealing with large-scale knowledge graphs, graph-structure methods often face problems such as low computational efficiency, high data sparsity, and high semantic ambiguity. In order to enhance the precision and efficacy of entity alignment, researchers have begun to explore how to utilize additional information associated with entities to enhance the performance of entity alignment. Existing work has demonstrated the impact of additional information on multiple tasks. For example, [PERSON] et al. [10] proposed that in the entity-alignment task, Hit Ratio@1 would drop by at least 30% if entity descriptions were ignored.