A Hierarchy-aware Entity Alignment Method for Educational Knowledge Graphs

Anting Li, Shisong Chen, Zhixu Li, Jianfeng Qu, Zhiang Yue, Jingping Liu

Abstract: Entity alignment aims to find equivalent entities in two heterogeneous knowledge graphs(KGs), serving as a crucial process in the integration of KGs. As for educational KGs, different from traditional KGs, they exhibit a hierarchical structure, including one-to-one, one-to-many, and many-to-one inclusion relationships among entities. However, current research is incapable of handling hierarchical structure, resulting in entity redundancy within the educational KGs. In this study, we create two educational KGs focused on junior mathematics from different textbook versions, providing a valuable benchmark for this research community. Subsequently, we design a hierarchy-aware entity alignment approach for educational KGs across different versions. Our approach employs Graph Convolutional Networks to iteratively learn entity embeddings using seed alignment. Additionally, we harness domain-specific data such as questions, to identify potentially aligned entities. Finally, we consider alignment status within the parent and child nodes and employ a BERT binary classification model to detect inclusion relationships among candidate entity pairs, which is crucial for identifying inclusion relationships between non-independent equivalent entities and entities within the target KG. The effectiveness of the proposed approach is demonstrated through comprehensive experiments and analyses.