Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the $21\mathrm{st}$ century. However, in many applications, users require an explanation of the Artificial Intelligence’s decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.

人工智能应用逐渐走出研究实验室的安全墙，侵入我们的日常生活。知识图谱上的机器学习方法也是如此，自人工智能诞生以来，其应用稳步增长。$21\mathrm{英石}$世纪。然而，在许多应用中，用户需要对人工智能的决定进行解释。这导致对可理解人工智能的需求增加。知识图体现了可理解人工智能的肥沃土壤，因为它们能够以人类和机器可读的方式显示互联数据，即知识。这项调查简要介绍了知识图谱上的可理解人工智能的历史。此外，我们认为“可解释的人工智能”这一概念是超载的，并且与可解释的机器学习有重叠。通过引入父概念“可理解的人工智能”，我们​​明确区分了这两个概念，同时考虑了它们的相似性。因此，我们在本次调查中提供了知识图谱上的可理解人工智能的案例​​，包括知识图谱上的可解释机器学习和知识图谱上的可解释人工智能。这导致在知识图谱上引入了一种新的可理解人工智能分类法。此外，还全面概述了知识图上的可理解人工智能的研究，并将其置于分类学的背景下。最后，确定了知识图上的可理解人工智能领域的研究空白以供未来研究。这导致在知识图谱上引入了一种新的可理解人工智能分类法。此外，还全面概述了知识图上的可理解人工智能的研究，并将其置于分类学的背景下。最后，确定了知识图上的可理解人工智能领域的研究空白以供未来研究。这导致在知识图谱上引入了一种新的可理解人工智能分类法。此外，还全面概述了知识图上的可理解人工智能的研究，并将其置于分类学的背景下。最后，确定了知识图上的可理解人工智能领域的研究空白以供未来研究。