Keywords: RML, Knowledge Graph Construction, RMLMapper, Challenge
TL;DR: RMLMapper supports all [R2]RML flavours through a translation layer!
Abstract: During the past decade, RML was proposed as an extension to the W3C’s R2RML Recommendation for supporting heterogeneous data sources. Although RML (RMLio flavour) was not a W3C Recommendation, it gained a lot of traction, and has been extended by the KG-Construct W3C Community Group as RMLkgc. Currently, this results in three main flavours (i.e. R2RML, RMLio, and KG-Construct’s RMLkgc) used among users of these mapping languages. Therefore, many existing mappings cannot be used among all existing [R2]RML engines, since they only implement one [R2]RML flavour. In this paper, we implement a translation of all flavours into the latest RML flavour (i.e. RMLkgc) within RMLMapper. This way, any mapping – no matter which flavour of [R2]RML was used – can be executed by RMLMapper. We discuss our translation approach and evaluate it in the KGCW Challenge 2024 Track 1 and all available RMLio and R2RML test cases to verify our translation into RMLkgc. We were able to translate R2RML and RMLio to RMLkgc Core (98,7%) and some parts of RMLkgc IO (50,75%) modules without changing the [R2]RML mappings. We reach a total coverage of 73,70% among all RMLkgc test cases and 100% coverage for RMLio and R2RML test cases. Thanks to our translation approach, we can re-use the same RMLMapper for all flavours without requiring the user to change their mappings. In the future, we aim to support all RMLkgc modules, while keeping support for the other flavours.
Submission Number: 12
Loading