They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
They work on structured data and therefore have the possibility to utilize comprehensive features like operators (e.g. >, < and =), namespaces, pattern matching, subclassing, transitive relations, semantic rules and contextual full text search.
[3][4] For example, the relationships between customers and products (stored in two content-tables and connected with an additional link-table) only come into existence in a query statement (SQL in the case of relational databases) written by a developer.
Now the system can automatically answer more complex queries and analytics that look for the connection of a particular location with a product category.
Executing a semantic query is conducted by walking the network of information and finding matches (also called Data Graph Traversal).