配对智能还是高阶智能? 一个自我适应的图表框架,用于知识图嵌入
Dong Zhang1, Haoqian Jiang1, Xiaoning Li1
1Information Science and Technology College, Dalian Maritime University, Dalian, 116026, Liaoning, China.
概括
本研究介绍了PHGCN,这是一种知识图嵌入的新型模型,有效地集成了对智和高阶特征,克服了图形卷积网络中改善人工智能应用的常见挑战.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 知识图 (KG) 对人工智能至关重要,但存在不完整的问题.
- 知识图嵌入 (KGE) 通过将实体和关系表示为向量来解决这个问题.
- 图形卷积网络 (GCN) 是关键的KGE模型,但面临着过度平滑和有限的功能集成等挑战.
研究的目的:
- 为了解决知识图表表示学习的GCN的局限性.
- 开发一个有效集成对智和高阶特征的模型.
- 提高知识图嵌入的准确性和性能.
主要方法:
- 拟议的PHGCN (对智和高序图卷积网络),是一种自适应的GCN模型.
- 利用层意识的GCN来减轻对智关系聚合中的过度平滑.
- 使用简化的复杂神经网络来提取高阶拓特征.
- 引入了一种自我适应的聚合机制,用于整合各种功能.
主要成果:
- 在四个基准数据集上,PHGCN取得了最先进的结果.
- 由于高阶特征提取,表现出显著的性能改进.
- 在FB15k-237上实现了1.5%的改善,在WN18RR上实现了6.1%的改善.
结论:
- PHGCN有效地克服了过度平滑,并整合了对智和高阶特征.
- 该模型在知识图嵌入任务中提供了卓越的性能.
- 强调高阶拓特征对于增强KG表示的重要性.
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