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The dissertation is written in terms of the e-Business and Digital Marketing master course of the International Hellenic University. During this Master Thesis we will try to conduct an analysis for one of the most common ways, for results presentation, despite the usual Google search results, which is knowledge graphs. Moreover, we will provide definitions of knowledge graphs and how they are operated and constructed. We will also observe where these knowledge graphs are applicable and what position they occupy in the technological development. Additionally, we will analyze what role do they play and how they affect Google’s search results, as well as Search Engine Optimization. It is important to examine how valid knowledge graphs are regarding the results they give back after a specific search and query. Finally, yet importantly, we will focus on how linked they are to machine learning and in what way they complete machine learning. Furthermore, we will point out some of the most known applications, use cases and tasks of these graphs and we will conduct a review and comparison of the most known knowledge tools that contribute to the implementation of knowledge graphs. Knowledge graph is a collection of interlinked entities, which can be described in real world as objects, situations, events or abstract concepts. In other words, a knowledge graph is a programmatic way to represent a knowledge field with the help of data interlinked, subject-matter experts and machine learning algorithms. One of the most known examples is the box that gives more information about a topic that somebody searched in Google’s results. In our literature review, we will cover all the aspects of knowledge graphs providing definitions and the way they are structured as well as the problems that these graphs try to solve. Additionally, we will see where the knowledge graphs are applicable and what knowledge tools they use. Lastly, we will implement a knowledge graph and we will provide recommendations for further research.