The inference layer supports rule-based reasoning, i.e. given a set of rules it computes all possible inferences on the given dataset. Technically, forward-chaining [1] is applied, i.e. it starts with the available data and uses inference rules to extract more data. This is sometimes also referred to as “materialization”.

Currently, three fixed rulesets are supported, namely RDFS, OWL-Horst, and OWL-EL. Later versions will contain a generic rule-based reasoner such that a user can define it’s own set of rules which will be used to materialize the given dataset.

[1] https://en.wikipedia.org/wiki/Forward_chaining

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