CELLMEA:A Collaboratively Enhanced Large Language Model-based Entity Alignment for aircraft fault maintenance

Aircraft fault knowledge graphs serve as a critical knowledge base for the intelligent maintenance and operations of aviation equipment. However, the entity alignment tasks in their construction remain overly dependent on manual annotation, leading to issues such as inconsistent annotation quality and low annotation efficiency. Unsupervised methods provide a promising solution and have garnered significant research interest. However, existing unsupervised entity alignment approaches often overlook the impact of noisy entities, presenting a significant challenge for aligning entities in aviation fault data. This paper proposes a solution by incorporating a large language model (LLM) into the entity alignment process for aircraft fault knowledge graphs. By leveraging the world knowledge encoded in the LLM, the approach enhances the performance of unsupervised entity alignment models. Specifically, we introduce the Collaboratively Enhanced-based Large Language Model Entity Alignment (CELLMEA), which utilizes data from the aircraft flight control system manual, fault analysis manual, and typical fault cases. The model’s architecture includes a multi-view semantic information embedding that integrates structural, relational, and semantic data. Additionally, we propose an adaptive method for mixing hard negative samples, which generates higher-quality negative entities by combining noisy negative samples with reliable ones. Furthermore, an incremental consistency regularization technique is introduced to progressively refine the robustness of pseudo-labeling within the CELLMEA model. Finally, experimental results on a flight control system entity alignment dataset demonstrate that CELLMEA outperforms all baseline models, achieving an MRR (Mean Reciprocal Rank) value of 0.917 ± 0.011. These results validate the model’s effectiveness in handling unlabeled data and lay the groundwork for the engineering of aircraft fault knowledge graphs.