RT Book,_Section
T1 The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF
A1 Iglesias-Molina, Ana
A1 Van Assche, Dylan
A1 Arenas-Guerrero, Julián
A1 De Meester, Ben
A1 Debruyne, Christophe
A1 Jozashoori, Samaneh
A1 Maria, Pano
A1 Michel, Franck
A1 Chaves Fraga, David
A1 Dimou, Anastasia
K1 Declarative Language
K1 R2RML
K1 RML
K1 Knowledge Graph
AB The Relational to RDF Mapping Language (R2RML) became a W3C Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond relational databases was swiftly explored. As a result, several extensions and new mapping languages were proposed to tackle the limitations that surfaced as R2RML was applied in real-world use cases. Over the years, one of these languages, the RDF Mapping Language (RML), has gathered a large community of contributors, users, and compliant tools. So far, there has been no well-defined set of features for the mapping language, nor was there a consensus-marking ontology. Consequently, it has become challenging for non-experts to fully comprehend and utilize the full range of the language’s capabilities. After three years of work, the W3C Community Group on Knowledge Graph Construction proposes a new specification for RML. This paper presents the new modular RML ontology and the accompanying SHACL shapes that complement the specification. We discuss the motivations and challenges that emerged when extending R2RML, the methodology we followed to design the new ontology while ensuring its backward compatibility with R2RML, and the novel features which increase its expressiveness. The new ontology consolidates the potential of RML, empowers practitioners to define mapping rules for constructing RDF graphs that were previously unattainable, and allows developers to implement systems in adherence with [R2]RML.
PB Springer
SN 978-3-031-47242-8
SN 978-3-031-47243-5
YR 2023
FD 2023-10-27
LK https://hdl.handle.net/10347/40338
UL https://hdl.handle.net/10347/40338
LA eng
NO Iglesias-Molina, A. et al. (2023). The RML Ontology: A Community-Driven Modular Redesign After a Decade of Experience in Mapping Heterogeneous Data to RDF. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14266. Springer, Cham. https://doi.org/10.1007/978-3-031-47243-5_9
NO Ana Iglesias-Molina is supported by the project Knowledge Spaces (Grant PID2020-118274RB-I00 funded by MCIN/AEI/10.13039/501100011033). Dylan Van Assche is supported by the Special Research Fund of Ghent University under grant BOF20/DOC/132. Ben de Meester is supported by SolidLab Vlaanderen (Flemish Government, EWI and RRF project VV023/10). Julián Arenas-Guerrero is partially supported by the Euratom Research and Training Programme 2019–2020 under grant agreement No 900018 (ENTENTE project). David Chaves-Fraga is partially supported by the Galician Ministry of Culture, Education, Professional Training, and University, by the European Regional Development Fund (ERDF/FEDER program) through grant ED431C2022/19 and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Politécnica de Madrid in the line Support for R &D projects for Beatriz Galindo researchers, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). Anastasia Dimou is partially supported by Flanders Make, the strategic research centre for the manufacturing industry and the Flanders innovation and entrepreneurship (VLAIO) via the KG3D project. The collaboration of Dylan Van Assche, Ben De Meester, Christophe Debruyne, David Chaves-Fraga and Anastasia Dimou is stimulated by the KG4DI FWO scientific research network (W001222N).
DS Minerva
RD 28 abr 2026