The DARE (Domain Adaptive Relation Extraction) System implements a minimally supervised machine learning framework for extracting relations of various complexity. It

• starts a bootstrapping from a small set of n-ary relation instances as "seeds"


• learns automatically pattern rules from parsed data, which can then extract new instances of the n-ary relation and its projections

DARE present a novel rule representation model which enables the composition of n-ary relation rules on top of the rules for projections of the relation. The compositional approach to rule construction is supported by a bottom-up pattern extraction method.