The precision of Google Search has developed a lot since all gathered knowledge is stored as a graph. The graph database is suitable for storing millions of nodes and edges. However, it is rather difficult to see through even a graph with twenty nodes and connections between them. It makes the search and the managing of the graphs difficult and complicated.
In my thesis, I have been dealing with a visualisation of a knowledge-graph, which gives back a subgraph if searching for an expression. Each point of a subgraph is a result and the edges are the connection between them. The examined knowledge-graph is based on scientific publications so in this case, the result of the search is a pile of articles related to this topic giving an overview on the field of research.
The nodes can be publications, keywords, authors and everything which describe the article. The edges could be references or a connection to a description node for example to a keyword node.
In my work, I have used public articles in order to build up a graph database and then I am examining which subgraph would be the most informative in case of the related search expression.
This new scientific point of view will help to improve in all field of science. As a result, remarkable change will happen to the human's way of thinking. The question is why this has never happened yet. We have used the graphs in many fields of science, but we have never used in everyday life.