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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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通过跨模式学习融合多模式内容,嵌入知识图
Shi Liu1, Kaiyang Li1, Yaoying Wang1
1Big Data Center of State Grid Corporation, Beijing 100052, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了一种新的多式联络内容融合 (MMCF) 模型,用于知识图嵌入. MMCF有效地整合了各种数据类型,大大提高了链接预测准确度,相比现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 传统上,知识图嵌入 (KGE) 依赖于来自三位数的结构信息.
- 现有的KGE方法往往忽略了丰富的实体和关系内容,例如文本描述和图像.
- 目前的多式联运KGE方法与数据异质性和跨式联运相关性作斗争.
研究的目的:
- 为增强知识图嵌入提出一种新的多式联络内容融合 (MMCF) 模型.
- 为了 KGE.有效地融合异构的多式联网数据 (文本,图像,结构).
- 通过纳入多式联络信息,提高实体和关系的代表性学习.
主要方法:
- 开发了一个交叉模式的相关性学习组件,以融合模式内和模式间的数据.
- 采用一个门网,以整合融合的多式联络内容与结构特征.
- 通过融合关联的头部和尾部实体的特征来增强关系嵌入.
主要成果:
- 建议的MMCF模型在链接预测任务中表现出卓越的表现.
- 在三个基准数据集 (FB-IMG,WN18RR,FB15k-237) 中观察到显著改善.
- 在大多数评估指标中,MMCF的表现优于最先进的基线方法.
结论:
- 货币货币基金模式通过利用多式联网内容有效地解决了现有的KGE方法的局限性.
- 拟议的交叉关联学习和融合战略提高了知识图嵌入的质量.
- 这些发现突显了多式联络融合在促进知识图表表示学习方面的潜力.
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