卷积神经网络知识图链接预测模型基于关系记忆
Ming Shi1, Jing Zhao1, Donglin Wu1
1School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Science), Jinan/250353, China.
Computational intelligence and neuroscience
|June 16, 2023
概括
这项研究引入了一种新的知识图嵌入模型 (RMCNN),通过整合关系记忆和卷积神经网络来增强链接预测. 该RMCNN模型显著提高不完整的知识图的推理能力.
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
- 计算机科学 计算机科学
背景情况:
- 知识图表将事实表示为三倍,形成语义网络.
- 链接预测旨在推断知识图中的缺失三位数.
- 现有的模型,如翻译和语义匹配在表达力上有局限性,而神经网络可能会忽视结构特征.
研究的目的:
- 为了解决当前知识图形链接预测模型的局限性.
- 提出一种新的知识图嵌入模型,RMCNN,结合关系记忆和卷积神经网络.
- 增强在低维空间中捕捉实体和关系链接的能力.
主要方法:
- 开发了一个知识图嵌入模型 (RMCNN),使用关系记忆网络进行编码和卷积神经网络进行解码.
- 编码实体和关系向量来捕捉潜在的依赖关系和转换属性.
- 组成的头实体,关系和尾实体嵌入到卷积神经网络输入的矩阵中.
- 采用一个维度转换策略来增强信息交互能力.
主要成果:
- 拟议的RMCNN模型在知识图链接预测方面取得了重大进展.
- 实验结果显示,与现有模型和方法相比,在几个指标上表现优越.
- 该模型有效地捕捉了实体和关系之间的结构特征和联系.
结论:
- RMCNN模型为知识图链接预测提供了一种有效的方法.
- 整合关系记忆网络和卷积神经网络可以增强对不完整知识图的推理.
- 拟议的方法推进了知识图嵌入和链接预测的最新技术.
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