  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.
