计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 172-180.doi: 10.11896/jsjkx.220500135
栗书敬, 黄增峰
LI Shujing, HUANG Zengfeng
摘要: 知识图谱方法与技术在人工智能领域有较高价值,其面临的一大难题是现有的知识图谱数据集中存在大量边缺失的现象,知识图谱表示学习为解决这一问题提供了解决方案。表示学习的质量取决于嵌入空间的几何形状与数据结构的匹配程度。欧氏空间一直是知识图谱表示学习的主力,而双曲和球面空间因其能够更好地嵌入新类型的结构数据而逐渐受到关注。但大多数数据的异质度较高,单一空间建模可能会导致信息失真较大。为了解决这个问题,受MuRP模型的启发,提出了用混合曲率空间来提供适合各种异质结构数据的表示,用欧氏、双曲和球面空间的笛卡尔积来构造混合空间;设计了混合空间的图注意力机制来获取关系的重要性。在知识图谱3个基准数据集上的实验结果表明,所提模型可以有效缓解异质结构嵌入常曲率低维空间导致的问题。将所提方法应用于推荐系统的冷启动问题上,相应指标均有一定程度的提高。
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