Knowledge Discovery in Wikidata with Machine Learning in Graph

Wikipedia contains a large amount of information distributed on many web pages and in several languages. As for a particular topic, the same thing happens, there is a lot of information on the same topic, and it is distributed on many web pages. When you need to know the number of elements of a nominal categorical variable or to count the elements of a category that are distributed in many wikidata web pages, then there is a difficulty, and you need to apply machine learning techniques. With Graph Machine learning, you can harness the power of representational learning and apply it to data extracted from wikidata, using graphs to find underlying structural similarities effectively and efficiently. In addition, the graphs provide us with an additional dimension to analyze the data. In this scientific article, the methodology to reach that goal is studied, and a particular topic has been taken to apply this methodology, and obtain the answers. The theme is “Search for similar Italian painters with their similar paintings in terms of material used and genre of art that are in museums around the world.”