Knowledge graphs are becoming an irreplacable components of machine intelligence. Their importance and potential for the future is the most obvious as Microsoft, Google, Facebook and other tech giants are investing more and more money in their use and development. Knowledge graphs are a sets of interconnected objects and events from real world or abstract concepts with a formal structure that allows both humans and machines to understand. Knowledge graphs are the main tool of the semantic web, which is an extension of the World Wide Web. On the semantic web, data is interconnected so that is machine-readable. Knowledge graphs and semantic web require technologije like: URI, XML, RDF, RDFS, OWL and SPARQL that provide the necessary description of entities and knowledge to help the machine to understand the data as closely as humans. As Wikipedia data was understandable to humans only, so on the principle of knowledge graphs Wikidata was created, a knowledge base designed to make information it contains legible to both humans and machines. This technology, with the help of structured computer queries defined by some of the query languages (eg SPARQL), allows Wikidata to create real-time answers (graphs of knowledge) at the very time when a query arrives at the knowledge base and therefore represent current knowledge that the technical system can use to conclude.