中文摘要：

知识图谱是在大数据时代背景下产生的一种新型知识表示方式和数据管理模式．学习和推理是知识图谱应用研究的核心内容之一，主要任务是链接预测、实体解析和基于聚类的链接等，它能够进一步完善知识图谱，并解决问题回答和信息检索等领域问题，因此，学习和推理的算法研究具有十分重要的意义．国内知识图谱研究和应用正处于开展阶段，学习和推理算法的中文文献相对较少，针对当前知识图谱的学习和推理算法进行了归纳总结和介绍，比较各种算法的优缺点，同时对当前研究中所面临的一些主要问题及发展方向进行了探讨．

英文摘要：

Knowledge graph, a production of big data era, was used as a new approach of knowledge representation and model of knowledge management. It was usually applied in areas of large scale knowledge graph completion, information retrieval, natural lan- guage processing and machine learning, etc. Knowledge graph learning and reasoning was shown as an effective way to solve these problems and was a core content in applications of knowledge graph meanwhile. Therefore, studies on algorithms of knowledge graph learning and reasoning had great significance for knowledge graph application. This paper firstly summarized and introduced the main tasks of those studies,including link prediction,entity resolution and Link-based clustering,etc. Then analyzed and compared the ad- vantages and disadvantages of various algorithms grouped by class ,based on the review and detailed introduction of the state-of-the-art algorithms of knowledge graph learning and reasoning. After that, it summarized the advantages and disadvantages of three typical clas- ses of algorithms. Moreover,it discussed the current major problems we are facing and the possible extensions. It suggested existing al- gorithms should improve predicting efficiency and accuracy, most of the knowledge of learning and inference algorithm should not just applied to binary relations, application fields should extend from general knowledge graph to professional areas ,and to pay more atten- tion on multi-data fusion reasoning and Chinese knowledge graph learning and reasoning.