  Global datasphere is increasing fast, and it is expected to reach 175
Zettabytes by 20251 . However, most of the content is unstructured and is not
understandable by machines. Structuring this data into a knowledge graph
enables multitudes of intelligent applications such as deep question answering,
recommendation systems, semantic search, etc. The knowledge graph is an
emerging technology that allows logical reasoning and uncovers new insights
using content along with the context. Thereby, it provides necessary syntax and
reasoning semantics that enable machines to solve complex healthcare, security,
financial institutions, economics, and business problems. As an outcome,
enterprises are putting their effort into constructing and maintaining
knowledge graphs to support various downstream applications. Manual approaches
are too expensive. Automated schemes can reduce the cost of building knowledge
graphs up to 15-250 times. This paper critiques state-of-the-art automated
techniques to produce knowledge graphs of near-human quality autonomously.
Additionally, it highlights different research issues that need to be addressed
to deliver high-quality knowledge graphs
