GraphDB supports inference out of the box and provides updates to inferred facts automatically. Facts change all the time and the amount of resources it would take to manually manage updates or rerun the inferencing process would be overwhelming without this capability. This results in improved query speed, data availability and accurate analysis. GraphDB will use the data and the rules to infer more facts and thus produce a richer data set than the one you started with. GraphDB can be configured via "rule-sets" – sets of axiomatic triples and entailment rules – that determine the applied semantics.

The Resource Description Framework, more commonly known as RDF, is a graph data model that formally describes the semantics, or meaning of information. It also represents metadata, that is, data about data. These triples are based on an Entity Attribute Value (EAV) model, in which the subject is the entity, the predicate is the attribute, and the object is the value. Each triple has a unique identifier known as the Uniform Resource Identifier, or URI. The parts of a triple, the subject, predicate, and object, represent links in a graph.

SPARQL is a SQL-like query language for RDF data. SPARQL queries can produce result sets that are tabular or RDF graphs depending on the kind of query used. Let's use SPARQL, the query language for RDF graphs, to create a graph. First, define prefixes to URIs with the PREFIX keyword. In the example below, we set bedrock as the default namespace for the query.