FAIR Linked Data - Towards a Linked Data Backbone for Users and Machines

Abstract

Although many FAIR principles could be fulfilled by 5-star Linked Open Data, the successful realization of FAIR poses a multitude of challenges. FAIR publishing and retrieval of Linked Data is still rather a FAIRytale than reality, for users and machines. In this paper, we give an overview on four major approaches that tackle individual challenges of FAIR data and present our vision of a FAIR Linked Data backbone. We propose 1) DBpedia Databus - a flexible, heavily automatable dataset management and publishing platform based on DataID metadata; that is extended by 2) the novel Databus Mods architecture which allows for flexible, unified, community-specific metadata extensions and (search/annotation) overlay systems; 3) DBpedia Archivo an archiving solution for unified handling and improvement of FAIRness for ontologies on publisher and consumer side; as well as 4) the DBpedia Global ID management and lookup services to cluster and discover equivalent entities and properties