  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.
