An effective link prediction method for industrial knowledge graphs by incorporating entity description and neighborhood structure information

The current industrial knowledge graph often faces the challenge of data sparsity, which can significantly impact its effectiveness and reliability in daily operational processes. To address this challenge and ensure the integrity of the knowledge graph, we propose a novel method for link prediction that leverages both entity descriptions and neighborhood structure information. Specifically, our method uses BERT pre-training to obtain meaningful embeddings from entity descriptions and the R-GCN model to capture the structural patterns within neighborhoods. Additionally, a CNN is employed to fuse and decode these two types of representations, ensuring high accuracy in predicting missing links. We have evaluated our method on publicly available datasets, and the experimental results show its superiority over baseline models. Furthermore, when tested on the SEFD dataset for steel fault diagnosis, our method effectively completes the knowledge graph for this industrial domain.