Knowledge Graphs (KGs) have been popularized during the last decade, for instance, they are used widely in the context of the web. In 2012 Google has presented the Google's Knowledge Graph that is used to improve their web search services. The web also hosts different KGs, such as DBpedia and Wikidata, which are used in various applications like personal assistants and question-answering systems. Various web applications rely on KGs to provide concise, complete, accurate, and fresh answer to users. However, what is the quality of those KGs? In which cases should a Knowledge Graph (KG) be used? How might they be evaluated? We reviewed the literature on quality assessment of data, information, linked data, and KGs. We extended the current state-of-the-art frameworks by adding various quality dimensions (QDs) and quality metrics (QMs) that are specific to KGs. Furthermore, we propose a general-purpose, customizable to a domain or task, and practical quality assessment framework for assessing the quality of KGs.