Knowledge graph, as a backbone of many information systems, has been created to organize the rapidly growing knowledge in a semantical and visualized manner. Symbolic reasoning and statistical reasoning are current mainstream techniques that play important roles in knowledge completion, automatic schema constructing, complex question answering, explanation of AI. However, both of them have their merits and limitations. Therefore, it is desirable to combine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on problem settings and reasoning tasks, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and outlook the future directions for hybrid reasoning in Knowledge graphs.

中文翻译：

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知识图中的混合推理：将符号推理和统计推理结合在一起

作为许多信息系统的骨干，知识图已经创建出来，以语义和可视化的方式组织迅速增长的知识。符号推理和统计推理是当前的主流技术，它们在知识完成，自动模式构建，复杂问题回答，人工智能解释中起着重要作用。但是，它们都有优点和局限性。因此，期望将它们组合以在知识图中提供混合推理。在本文中，我们提出了关于知识图混合推理方法研究的第一项工作。我们根据问题设置和推理任务对现有方法进行分类，并介绍它们的关键思想。最后，