External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with a few classic and state-of-the-art methods for each task, including a method with KG augmented explanation. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.

外部知识（又名辅助信息）在零样本学习 (ZSL) 中起着至关重要的作用，零样本学习旨在预测从未出现在训练数据中的看不见的类别。文本和属性等几种外部知识已被广泛研究，但仅它们本身就存在语义不完整的限制。因此，一些最近的研究建议使用知识图（KG），因为它具有高表达性和兼容性来表示各种知识。然而，ZSL 社区仍然缺乏用于研究和比较不同外部知识设置和不同基于 KG 的 ZSL 方法的标准基准。在本文中，我们提出了六种资源，涵盖三个任务，即零样本图像分类（ZS-IMGC）、零样本关系提取（ZS-RE）和零样本知识图谱完成（ZS-KGC）。每个资源都有一个正常的 ZSL 基准和一个包含从文本到属性、从关系知识到逻辑表达式的语义的 KG。我们已经清楚地展示了这些资源，包括它们的构建、统计、数据格式和不同 ZSL 方法的使用案例。更重要的是，我们进行了全面的基准测试研究，为每个任务使用了一些经典和最先进的方法，包括 KG 增强解释的方法。我们讨论并比较了不同外部知识设置的不同 ZSL 范式，发现我们的资源在开发更先进的 ZSL 方法和更多应用 KG 增强机器学习的解决方案方面具有巨大潜力。所有资源均可在 https://github.com/China-UK-ZSL/Resources_for_KZSL 获得。