多元期货基金:多层次的结构特征融合为多模式知识图的完成
Hanming Zhai1, Xiaojun Lv2, Zhiwen Hou1
1School of Information Network Security, People's Public Security University of China, Beijing 100038, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究引入了一种新的多模式知识图完成方法. 它通过学习多层次图形结构来提高推理准确性,提高知识图形的完整性.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 多模式知识图对于存储人类知识至关重要.
- 知识图的不完整性是一个重大挑战,需要知识图完成 (KGC).
- 现有的KGC方法缺乏可解释性,并与多跳式推理作斗争.
研究的目的:
- 提出一种用于多模态知识图完成的新方法.
- 为了提高推理的准确性和可解释性在多模式知识图.
- 解决当前基于嵌入和图形神经网络方法的局限性.
主要方法:
- 使用变压器架构进行单独的图像和文本模式表示学习.
- 采用多式联网网关单元进行信息过和功能融合.
- 提取多层次路径特征并应用结构学习的图形卷积运算.
主要成果:
- 在FB15K-237-IMG数据集上的Hits@1实现了1.8%的改进.
- 在WN18-IMG数据集上的Hits@1中实现了0.7%的改进.
- 在多模式知识图完成任务中表现出有效性.
结论:
- 提出的方法有效地学习了多层图的结构特征.
- 这种方法可以提高推理准确度,以完成多模式知识图.
- 这项工作为提高知识图的完整性和实用性提供了一个有希望的方向.
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