The constant flow of information allows the data network to be accurately depicted as a graph, illustrating the interconnections across various groups, including items, customers, transactions, categories, reviews, and suppliers. Graph databases are proficient at revealing relationships and patterns in domains such as social networks, recommendation systems, the semantic web, and fraud detection. They facilitate intricate inquiries, including the determination of shortest paths, the identification of pivotal hubs, and the analysis of relationships. Although graph databases exhibit significant scalability and effectively handle extensive quantities of interconnected data, they frequently lack semantic Information, formal structure, and explicit typing. Knowledge graphs (KGs) mitigate these restrictions by structuring data in a graph format and linking data points via relationships to encapsulate Information. This chapter seeks to elucidate the functionalities of KGs, encompassing activities from traditional graph configurations to semantic frameworks, scalability issues, and query methodologies.