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基于异质图形神经网络的作者名称清晰化
Ge Wang1, Zikai Sun1, Weiyang Hu1
1College of Intelligent Equipment, Shandong University of Science and Technology, Taian, Shandong, China.
PloS one
|February 26, 2025
概括
本研究介绍了一种使用关系图异质注意力神经网络的新型作者名称清晰化方法. 这种方法提高了分配论文的准确性和效率,优于现有的技术.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 越来越多的学术出版物和作者重叠对准确的作者名称清晰化提出了重大挑战.
- 现有的基于特征和基于连接的集群方法在解决作者名称模糊性的有效性和准确性方面扎.
研究的目的:
- 开发一种先进的作者名称清晰化方法,克服当前方法的局限性.
- 为了提高新发表的论文分配到正确的作者准确性和效率.
主要方法:
- 提出一个关系图异质注意力神经网络,从论文中提取和表示语义和关系信息.
- 结合多个注意力机制,以增强在图中各种节点和边界相互作用的学习.
- 通过使用学习的向量表示和自动确定最佳集群数量 (k-值) 来修改层次的集群.
主要成果:
- 拟议的方法在Aminer数据集上获得了0.834的平均F1得分.
- 与现有的主流作者清晰化方法相比,表现优越.
- 废弃实验证实了多重注意力机制对网络性能的积极影响.
结论:
- 关系图异质注意力神经网络为作者名称的明确化提供了更准确和更有效的解决方案.
- 集成图形拓和学习表征显著提高了集群精度和自动化.
- 这种方法有效地解决了学术数据库中作者名称模糊性日益增长的挑战.
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