Knowledge Graph (KG) is a graph based data structure to represent facts of
the world where nodes represent real world entities or abstract concept and
edges represent relation between the entities. Graph as representation for
knowledge has several drawbacks like data sparsity, computational complexity
and manual feature engineering. Knowledge Graph embedding tackles the drawback
by representing entities and relation in low dimensional vector space by
capturing the semantic relation between them. There are different KG embedding
models. Here, we discuss translation based and neural network based embedding
models which differ based on semantic property, scoring function and
architecture they use. Further, we discuss application of KG in some domains
that use deep learning models and leverage social media data.

知识图谱是一个基于图形的数据结构，用于表示世界的事实，其中节点表示现实世界的实体或抽象概念，边表示实体之间的关系。知识图谱嵌入通过在低维向量空间中表示实体和关系来捕捉它们之间的语义关系。本文讨论了基于翻译和神经网络的嵌入模型，它们根据语义属性、评分函数和架构的不同而有所区别，并进一步讨论了在一些使用深度学习模型和利用社交媒体数据的领域中应用知识图谱。