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本文是对知识图谱相关文章阅读翻译，《Knowledge Graph Embedding: A Survey of Approaches and Applications》。
在系统回顾了当前可用的KG嵌入技术之后，本节探讨了如何将学习到的实体和关系嵌入应用于多种下游任务并从中受益。我们将这些任务分为（i）KG内应用程序和（ii）KG外应用程序，讨论如下。
KG中的应用是在KG范围内进行的，其中实体和关系嵌入都是通过学习的。我们介绍了四种这样的应用，即链路预测、三重分类、实体分类和实体解析，这些在文献中得到了广泛的研究。所有这些应用都是从不同的角度和应用上下文对输入KG的各种细化（例如，完成或消除重复）。
链接预测通常被称为预测与另一给定实体具有特定关系的实体的任务，比如给定 ( r , t ) (r,t) (r,
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知识图谱（Knowledge Graph or KG），如： Free Base、DBpedia、YAGO、NELL等已经成功地应用到语义分析、信息抽取、问答系统等方面。知识图谱是由实体（entity）和关系（relations: 不同类型的边）构成的多关系图。每一条边都以三元组的形式呈现（head entity, relation, tail entity），这也叫做fact。 KG Embed
知识图谱经典方法 Knowledge Graph Embedding：从TransE，TransH到TransR
1.背景 KG Embedding可以建立实体到实体的关联。
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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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