Research on knowledge graph alignment model based on deep learning
作者：
Highlights：
• We propose a novel knowledge graph alignment model based on deep learning.
• The experiments reveal that our model has effectively improved the performance.
• We propose a novel negative sampling method, i.e., transformation negative sampling.
• We examine the key influencing factors of knowledge graph alignment.
• Our research has practical implications for improving the alignment performance.
摘要
•We propose a novel knowledge graph alignment model based on deep learning.•The experiments reveal that our model has effectively improved the performance.•We propose a novel negative sampling method, i.e., transformation negative sampling.•We examine the key influencing factors of knowledge graph alignment.•Our research has practical implications for improving the alignment performance.
论文关键词：Deep learning,Domain knowledge alignment,Knowledge graph,Knowledge representation
论文评审过程：Received 28 April 2020, Revised 7 December 2020, Accepted 12 August 2021, Available online 20 August 2021, Version of Record 26 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115768
