Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrinsic complexity of inter-class relationships represented in KGs. One typical feature is that a class is often related to other classes in different semantic aspects. In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects. We also contribute a new ZSL framework named DOZSL, which contains two new ZSL solutions based on generative models and graph propagation models, respectively, for effectively utilizing the disentangled ontology embeddings. Extensive evaluations have been conducted on five benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). DOZSL often achieves better performance than the state-of-the-art, and its components have been verified by ablation studies and case studies. Our codes and datasets are available at https://github.com/zjukg/DOZSL.
翻译:知识图(KG)及其本体学变体被广泛用于知识代表,并表明在加强零光学习(ZSL)方面相当有效。然而,现有的ZSL方法使用KGs,这些方法都忽视了KGs所代表的阶级间关系的内在复杂性。一个典型特征是,一个班级往往与其他班级在不同语义方面有联系。在本文件中,我们侧重于加强ZSL的理论,并提议学习由本体特性指导的分解本体嵌入,以捕捉和利用不同方面的更精细的阶级关系。我们还贡献了一个名为DZSL的新ZSL框架,它分别包含基于基因模型和图表传播模型的两种新的ZSL解决方案,以便有效利用分解的语义嵌入。我们对零光图像分类(ZS-IMGC)和零光KG完成(ZS-KGC)的五个基准进行了广泛的评价。DSLS往往比州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州/州研究的案例研究)的案例研究)的案例研究的案例研究的案例研究的案例研究)的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究的案例研究研究,已经验证的案例研究的案例研究的