One of the promising approaches to address the issues associated with Big Data is to store data as structured data and represent the
as graphs, which allows software agents to query these semantic databases and yield enriched results. The processing of
makes it possible to find information currently difficult or impossible to find as well as to find further, related information.
A great example of Big Data applications on the Semantic Web is the
of Google, which was introduced in 2012. The
Google Knowledge Graph
is a semantic knowledge base to enhance traditional Search Engine Result Pages (SERPs) with semantic search information gathered from a wide variety of sources. The result of a Knowledge Graph search is not only relevant information far more accurate that what you would find with traditional searches, but also related information such as similar resources people search for the most. For example, if you search for the title of an action movie, the results will include similar movies, while searching for a particular inventor will show further inventors with similar research fields and awards. The
contains more than half a billion objects and over 18 billion facts about relationships between different objects that help software agents “understand” the meaning of the search keywords, and these figures are constantly growing.