A vocabulary-independent generation framework for DBpedia and beyond

The dbpedia Extraction Framework, the generation framework behind one of the Linked Open Data cloud's central hubs, has limitations which lead to quality issues with the dbpedia dataset. Therefore, we provide a new take on its Extraction Framework that allows for a sustainable and general-purpose Linked Data generation framework by adapting a semantic-driven approach. The proposed approach decouples, in a declarative manner, the extraction, transformation, and mapping rules execution. This way, among others, interchanging different schema annotations is supported, instead of being coupled to a certain ontology as it is now, because the dbpedia Extraction Framework allows only generating a certain dataset with a single semantic representation. In this paper, we shed more light to the added value that this aspect brings. We provide an extracted dbpedia dataset using a different vocabulary, and give users the opportunity to generate a new dbpedia dataset using a custom combination of vocabularies.