Machine learning (ML), especially deep neural networks, has 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, ML 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 articles 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.

中文翻译：

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使用知识图进行零样本和少样本学习：综合调查


机器学习（ML），尤其是深度神经网络，已经取得了巨大的成功，但其中许多往往依赖于大量标记样本进行监督。由于实际应用中不断出现的预测目标和昂贵的样本注释等原因，并不总是准备好足够的标记训练数据，因此样本短缺的机器学习现在正在被广泛研究。在所有这些研究中，许多研究更喜欢利用辅助信息，包括知识图谱（KG）形式的辅助信息来减少对标记样本的依赖。在这项调查中，我们全面回顾了 90 多篇关于 KG 感知研究的文章，涉及两个主要样本短缺的情况：零样本学习（ZSL），其中一些要预测的类没有标记样本；以及少样本学习（FSL），其中一些要预测的类没有标记样本。要预测的类只有少量可用的标记样本。我们首先介绍ZSL和FSL中使用的KG及其构建方法，然后系统地分类和总结KG感知的ZSL和FSL方法，将它们分为不同的范式，例如基于映射的、数据增强的、基于传播的，并基于优化。接下来我们介绍不同的应用，不仅包括图像分类、问答、文本分类和知识提取等知识图谱增强预测任务，还包括知识图谱补全任务以及每个任务的一些典型评估资源。我们最终从不同的角度讨论一些挑战和开放性问题。