Towards a more human-friendly knowledge graph generation & publication
Dylan Van Assche, Thomas Delva, Pieter Heyvaert, Ben De Meester, Anastasia Dimou · Lirias · 2021
Human-friendly representations of mapping languages, e.g., YARRRML or ShExML, allow seamlessly integrating heterogeneous data into knowledge graphs.However, they do not describe how a generated knowledge graph should be exported or published, leaving the knowledge graph generation and publication disconnected.To address this, we recently introduced the Logical Target in RML, but an alignment with the human-friendly representations of mapping languages is still pending.In this demo paper, we (i) align RML's Logical Target with YARRRML to provide a more human-friendly syntax for knowledge graph's generation and publication and (ii) demonstrate our approach in YARRRML's editor Matey.YARRRML users are now able to describe how their generated knowledge graphs are exported to one or multiple targets, paving the path towards a more reproducible and human-friendly workflow for generating and publishing knowledge graphs.