  Knowledge Graphs (KGs) represent real-world noisy raw information in a
structured form, capturing relationships between entities. However, for dynamic
real-world applications such as social networks, recommender systems,
computational biology, relational knowledge representation has emerged as a
challenging research problem where there is a need to represent the changing
nodes, attributes, and edges over time. The evolution of search engine
responses to user queries in the last few years is partly because of the role
of KGs such as Google KG. KGs are significantly contributing to various AI
applications from link prediction, entity relations prediction, node
classification to recommendation and question answering systems. This article
is an attempt to summarize the journey of KG for AI.
