  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.
