通过子图和类型信息的组合嵌入感应知识图
Hongbo Liu1, Yue Chen2, Peng He3
1Information Engineering University, Zhengzhou, 450001, China. lhb921@163.com.
Scientific reports
|December 1, 2023
概括
本研究介绍了TGraiL,这是一个用于知识图的感应表示学习模型. 通过整合拓结构和语义信息,TGraiL有效地处理与未见实体的不断演变的知识图,以改进链接预测.
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
- 机器学习 机器学习
背景情况:
- 传统的知识图表表示方法将项目实体和关系转化为矢量空间,增强链接预测和下游任务.
- 这些方法与知识图的演变作斗争,未能在目标知识图中处理以前未见的实体.
- 目前用于链接预测的基于感应子图的模型忽略了关键的语义信息.
研究的目的:
- 开发一种诱导式表示学习模型,能够处理与看不见实体的不断变化的知识图.
- 整合拓结构和语义信息,以实现更强大的知识图表表示.
- 在动态知识图环境中提高链接预测的性能.
主要方法:
- 提出TGraiL,一种用于知识图的感应性表示学习模型.
- 使用子图距离编码节点拓结构.
- 通过投影矩阵编码实体类型信息.
- 融合拓和语义信息用于训练实体向量表示.
主要成果:
- 与现有的基线模型相比,TGraiL表现显著改善了性能.
- 该模型有效地整合了拓结构和语义信息,以实现增强的表示.
- 实验结果验证了提议的TGraiL方法的有效性和优越性.
结论:
- 在不断发展的知识图表中,TGraiL为诱导式表示学习提供了一个有效的解决方案.
- 拓和语义信息的整合对于处理看不见的实体至关重要.
- 提出的方法推进了知识图表表示学习和链接预测的最新技术.
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