SCMEA:基于多方面信息融合和双向对比学习的实体对齐的堆叠并加增强模型
Yunfeng Zhou1, Cui Zhu1, Wenjun Zhu1
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100020, China.
概括
本研究介绍了SCMEA,这是一个跨语言实体对齐的新框架,它融合了多方面的信息,并使用双向对比学习. 该方法通过解决缺少的信息和增强实体表示,有效地改善了知识图的完成和融合.
科学领域:
- 人工智能的人工智能
- 知识表示和推理.
背景情况:
- 实体对齐对于知识图完成和融合至关重要.
- 使用知识表示学习的现有方法在挖掘隐藏信息方面存在局限性.
研究的目的:
- 提出SCMEA,一个新的跨语言实体调整框架.
- 通过有效地挖掘多方面信息和改进实体表示来增强实体对齐.
主要方法:
- SCMEA采用多种代表性学习模型来嵌入多方面信息.
- 一个自适应加权机制统一嵌入,克服缺失和不统一的信息.
- 一个堆叠的关系实体共同增强模型与全球实体注意力完善实体表示.
- 改进的双向对比学习优化了参数,并减轻了中心性问题.
主要成果:
- 在跨语言实体对齐方面,SCMEA 显示了显著的性能改进.
- 在五个数据集上进行的废弃研究和实验验证实了该模型的有效性和稳定性.
- 该框架成功地解决了缺少信息和中心性问题等挑战.
结论:
- SCMEA为跨语言实体对齐提供了一个强大而有效的解决方案.
- 拟议的多方面信息融合和双向对比学习显著推进了这一领域.
- 该框架显示了知识图表完成和融合应用的巨大潜力.
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