Entity alignment method for aeronautical metrology domain based on multi-perspective entity embedding

The accuracy and consistency of metrology data are the cornerstones of the safety and reliability of aircraft throughout aeronautical products’ lifecycles. Due to the heterogeneous nature of metrology data derived from various sources, knowledge silos commonly emerge, complicating the integration and reuse of knowledge. This study introduces an entity alignment model leveraging multi-perspective embedding. It employs a multi-scale graph convolutional network enhanced by a gating mechanism that aggregates multi-hop neighborhood features to capture the structural embeddings of nodes. Additionally, the model utilizes TransD for representing complex relationships and BERT for capturing entity attributes, facilitating more comprehensive entity representations. Entity alignment is then accomplished by integrating structural, relational, and attribute embeddings using a weighted strategy. In this study, we conducted experimental validation on aeronautical metrology data and also assessed our proposed model on five benchmark datasets. The results indicate that our model significantly outperforms comparative models, demonstrating its potential to enhance the management and application of aeronautical metrology data.