# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 4 Experiments
### Experimental Results  
We summarize the results of our model with other baseline models on the MED-BBK-9K datasets in Table 3, where the experimental results of BootEA, RDGCN, GCN-Align and OntoEA are from [PERSON] et al. [29]. We classified these models into three categories: utilizing only graph-structure information, combining graph-structure information and additional information, and utilizing only additional information. **Only graph-structure information. ** BootEA [27] uses TransE to represent the semantics of entities and relationships in the knowledge graph, and this approach captures the diverse relational information between entities in a knowledge graph well. In order to better capture the common semantics among different knowledge graphs, the model also proposes a new objective function that requires absolutely low scores for the positive-example triples, which helps to reduce the embedding bias in the uniform space. RDGCN [8] constructs a dual graph structure, including entity and relationship graphs, to better capture relational information by allowing interaction between the two graphs, effectively integrating complex relational information into the entity representation. This model applies a GCN layer with highway gates on the entity graph to better integrate neighboring structural information. **Graph-structure information and additional information. ** GCN-Align [14] uses GCN to embed entities from different graphs into a unified vector space with the expectation that equivalent entities are close to each other. The method utilizes structural information (entity relationships) and attribute information associated with entities to improve the alignment performance. Compared with other embedding-based methods, this method does not require relationships or attributes to be aligned between atlases, and the model complexity is lower. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**MED-BBK-9K**} \\ \cline{2-5}  & **Hits@1** & **Hits@5** & **MRR** \\ \hline \multirow{2}{*}{Only graph-structure information.} & BootEA & 30.7 & 49.5 & 0.399 \\  & RDGCN & 30.6 & 42.5 & 0.365 \\ \multirow{2}{*}{Graph-structure information and additional information.} & GCN-Align & 6.5 & 15.3 & 0.117 \\  & OntoEA & 51.7 & **70.3** & 0.604 \\ \multirow{2}{*}{Only additional information.} & BERT-INT & 53.8 & 60.2 & 0.567 \\  & RoBERTa-INT & **59.2** & 66.8 & **0.625** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results from different entity-alignment models applied to the MED-BBK-9K datasets. The best is bold. OntoEA [29] is an ontology-guided knowledge-graph-alignment technique that explicitly models category conflict relationships to enrich the embedded semantic representation and avoid incorrect mappings. To model category-conflict relationships, it generates a category-conflict matrix to represent different types of category conflicts. In this process, it employs a simple method to introduce entity name information, using pre-trained word embeddings to initialize the vector representation of entities or ontologies. **Only additional information. ** BERT-INT [11] uses a pre-trained BERT model to encode the name/description of entities, obtains representation vectors of the entities, and aggregates features from different views in an interaction model in order to estimate the matching scores of the entities. The model benefits from the powerful semantic modeling capability of BERT and the effectiveness of the interaction mechanism to achieve better performance on the entity-alignment task. The experimental results show that our model achieves the best Hits@1 and MRR on the Chinese medical datasets MED-BBK-9K.