Ruben Verborgh’s data

Matches in Ruben’s data for { ?s ?p "DBpedia EF, the generation framework behind one of the Linked Open Data cloud’s central interlinking hubs, has limitations with regard to quality, coverage and sustainability of the generated dataset. DBpedia can be further improved both on schema and data level. Errors and inconsistencies can be addressed by amending (i) the DBpedia EF; (ii) the DBpedia mapping rules; or (iii) Wikipedia, from which it extracts information. However, even though the DBpedia EF and mapping rules are continuously evolving and several changes were applied to both of them, there are no significant improvements on the DBpedia dataset since its limitations were identified. To address these shortcomings, we propose adapting a different semantic-driven approach that decouples, in a declarative manner, the extraction, transformation and mapping rule execution. In this paper, we discuss the new DBpedia EF, its architecture, its technical implementation, and extraction results. The extraction time remains within the same magnitude, but the resulting extraction process is more sustainable. This way, we achieve an enhanced data generation process that can be broadly adopted, and which improves DBpedia’s quality, coverage, and sustainability."@en }

• 978-3-319-68204-4_28 abstract "DBpedia EF, the generation framework behind one of the Linked Open Data cloud’s central interlinking hubs, has limitations with regard to quality, coverage and sustainability of the generated dataset. DBpedia can be further improved both on schema and data level. Errors and inconsistencies can be addressed by amending (i) the DBpedia EF; (ii) the DBpedia mapping rules; or (iii) Wikipedia, from which it extracts information. However, even though the DBpedia EF and mapping rules are continuously evolving and several changes were applied to both of them, there are no significant improvements on the DBpedia dataset since its limitations were identified. To address these shortcomings, we propose adapting a different semantic-driven approach that decouples, in a declarative manner, the extraction, transformation and mapping rule execution. In this paper, we discuss the new DBpedia EF, its architecture, its technical implementation, and extraction results. The extraction time remains within the same magnitude, but the resulting extraction process is more sustainable. This way, we achieve an enhanced data generation process that can be broadly adopted, and which improves DBpedia’s quality, coverage, and sustainability.".