  Zero-shot Learning (ZSL), which aims to predict for those classes that have
never appeared in the training data, has arisen hot research interests. The key
of implementing ZSL is to leverage the prior knowledge of classes which builds
the semantic relationship between classes and enables the transfer of the
learned models (e.g., features) from training classes (i.e., seen classes) to
unseen classes. However, the priors adopted by the existing methods are
relatively limited with incomplete semantics. In this paper, we explore richer
and more competitive prior knowledge to model the inter-class relationship for
ZSL via ontology-based knowledge representation and semantic embedding.
Meanwhile, to address the data imbalance between seen classes and unseen
classes, we developed a generative ZSL framework with Generative Adversarial
Networks (GANs). Our main findings include: (i) an ontology-enhanced ZSL
framework that can be applied to different domains, such as image
classification (IMGC) and knowledge graph completion (KGC); (ii) a
comprehensive evaluation with multiple zero-shot datasets from different
domains, where our method often achieves better performance than the
state-of-the-art models. In particular, on four representative ZSL baselines of
IMGC, the ontology-based class semantics outperform the previous priors e.g.,
the word embeddings of classes by an average of 12.4 accuracy points in the
standard ZSL across two example datasets (see Figure 4).
