Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey

TL;DR

This survey comprehensively reviews how knowledge graphs are utilized to improve zero-shot and few-shot learning, addressing the challenge of limited labeled data in various AI tasks.

Contribution

It systematically categorizes and summarizes over 90 KG-aware methods for zero-shot and few-shot learning, covering construction, paradigms, applications, and open challenges.

Findings

Identifies key paradigms like mapping, data augmentation, propagation, and optimization.

Highlights diverse applications including image classification, QA, and text classification.

Discusses challenges and future directions in KG-aware learning.

Abstract

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.