Machine learning methods especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for training. In real-world applications, we often need to address sample shortage due to e.g., dynamic contexts with emerging prediction targets and costly sample annotation. Therefore, low-resource learning, which aims to learn robust prediction models with no enough resources (especially training samples), is now being widely investigated. Among all the low-resource learning studies, many prefer to utilize some auxiliary information in the form of Knowledge Graph (KG), which is becoming more and more popular for knowledge representation, to reduce the reliance on labeled samples. In this survey, we very comprehensively reviewed over $90$ papers about KG-aware research for two major low-resource learning settings -- zero-shot learning (ZSL) where new classes for prediction have never appeared in training, and few-shot learning (FSL) where new classes for prediction have only a small number of labeled samples that are available. We first introduced the KGs used in ZSL and FSL studies as well as the existing and potential KG construction solutions, and then systematically categorized and summarized 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 presented different applications, including not only KG augmented tasks in Computer Vision and Natural Language Processing (e.g., image classification, text classification and knowledge extraction), but also tasks for KG curation (e.g., inductive KG completion), and some typical evaluation resources for each task. We eventually discussed some challenges and future directions on aspects such as new learning and reasoning paradigms, and the construction of high quality KGs.
翻译:机械学习方法,特别是深层神经网络取得了巨大成功,但其中许多人往往依赖一些标签标注的样本来进行培训。在现实世界的应用中,我们常常需要解决由于以下两种主要低资源学习环境 -- -- 新预测目标的动态背景和昂贵的抽样说明 -- -- 低资源学习,目的是学习没有足够资源(特别是培训样本)的稳健预测模型。在所有低资源学习研究中,许多人更愿意使用一些以知识表示方式(KG)为形式的辅助信息,而KG正在日益普及,以减少对标签样本的依赖。在本次调查中,我们非常全面地审查了超过90美元的关于KG-aware研究的论文,涉及两个主要的低资源学习环境 -- -- 零点学习,在培训中从未出现新的预测课程(特别是培训样本),而新的预测班只有少量基于标签的样本。我们首先介绍了在ZSL和FSL研究中使用的KG分类, 以及现有的和潜在的KG构建解决方案,然后系统地对KG-G的排序和升级任务进行了分类。我们把KG-G的升级任务和升级任务作为不断升级的升级的模型,这些任务,作为不断升级和升级的升级的模型,作为KSLAlial-L任务。