Semantically Enabled Content Convergence System for Large Scale RDF Big Data

The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.