CELLMEA:A Collaboratively Enhanced Large Language Model-based Entity Alignment for aircraft fault maintenance
Authors:
- Xiangzhen MengXMXiangzhen Meng
- Xiaoxuan JiaoXJXiaoxuan Jiao
- Jiahui LiJLJiahui Li
- Shenglong WangSWShenglong Wang
- Jinxin PanJPJinxin Pan
- Bo JingBJBo Jing
- Xilang TangXTXilang Tang
- 21 April 2025
- journal article
- Published by Elsevier in Expert Systems with Applications
- Vol. 282
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (722012765)
This publication has 21 references indexed in Scilit:
- AdaMetaSRE: An adaptive meta semi-supervised relation extraction model for aircraft fault maintenanceExpert Systems with Applications, 2025
- GS-CBR-KBQA: Graph-structured case-based reasoning for knowledge base question answeringExpert Systems with Applications, 2024
- KGroot: A knowledge graph-enhanced method for root cause analysisExpert Systems with Applications, 2024
- A review of recommender systems based on knowledge graph embeddingExpert Systems with Applications, 2024
- Two-stage attention network for fault diagnosis and retrieval of fault logsExpert Systems with Applications, 2024
- SMAAMA: A named entity alignment method based on Siamese network character feature and multi-attribute importance feature for Chinese civil aviationJournal of King Saud University Computer and Information Sciences, 2023
- Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processesExpert Systems with Applications, 2023
- A benchmark and comprehensive survey on knowledge graph entity alignment via representation learningThe VLDB Journal, 2022
- Learning structured embeddings of knowledge graphs with generative adversarial frameworkExpert Systems with Applications, 2022
- An end-to-end tabular information-oriented causality event evolutionary knowledge graph for manufacturing documentsAdvanced Engineering Informatics, 2021