Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict missing information, recommender systems, question answering, query expansion, etc. The information embedded in Knowledge graph though being structured is challenging to consume in a real-world application. Knowledge graph embedding enables the real-world application to consume information to improve performance. Knowledge graph embedding is an active research area. Most of the embedding methods focus on structure-based information. Recent research has extended the boundary to include text-based information and image-based information in entity embedding. Efforts have been made to enhance the representation with context information. This paper introduces growth in the field of KG embedding from simple translation-based models to enrichment-based models. This paper includes the utility of the Knowledge graph in real-world applications.

中文翻译：

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知识图谱嵌入及其应用综述

知识图嵌入提供了一种用于表示知识的通用技术。这些技术可用于各种应用，例如知识图谱的补全以预测缺失信息、推荐系统、问答、查询扩展等。 知识图谱中嵌入的信息虽然是结构化的，但在现实世界中难以消费应用。知识图嵌入使现实世界的应用程序能够使用信息来提高性能。知识图谱嵌入是一个活跃的研究领域。大多数嵌入方法都侧重于基于结构的信息。最近的研究已经将边界扩展到实体嵌入中包括基于文本的信息和基于图像的信息。已经努力用上下文信息来增强表示。本文介绍了 KG 嵌入领域从简单的基于翻译的模型到基于丰富的模型的增长。本文包括知识图谱在实际应用中的实用性。