  Machine learning especially deep neural networks have achieved great success
but many of them often rely on a number of labeled samples for supervision. As
sufficient labeled training data are not always ready due to e.g., continuously
emerging prediction targets and costly sample annotation in real world
applications, machine learning with sample shortage is now being widely
investigated. Among all these studies, many prefer to utilize auxiliary
information including those in the form of Knowledge Graph (KG) to reduce the
reliance on labeled samples. In this survey, we have comprehensively reviewed
over 90 papers about KG-aware research for two major sample shortage settings
-- zero-shot learning (ZSL) where some classes to be predicted have no labeled
samples, and few-shot learning (FSL) where some classes to be predicted have
only a small number of labeled samples that are available. We first introduce
KGs used in ZSL and FSL as well as their construction methods, and then
systematically categorize and summarize KG-aware ZSL and FSL methods, dividing
them into different paradigms such as the mapping-based, the data augmentation,
the propagation-based and the optimization-based. We next present different
applications, including not only KG augmented prediction tasks such as image
classification, question answering, text classification and knowledge
extraction, but also KG completion tasks, and some typical evaluation resources
for each task. We eventually discuss some challenges and open problems from
different perspectives.
