Abstract 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. Resource type: Ontology/License: CC BY 4.0 International DOI: 10.5281/zenodo.7918478 /URL: http://w3id.org/rml/portal/

e-Government applications have hard-coded and non-personalized user journeys with high maintenance costs to keep up with, e.g., changing legislation. Automatic administrative workflows are needed. We present the OSLO-steps vocabulary and the workflow composer: combined, they are a means to create cross-organizational interoperable user journeys, adapted to the user's needs. We identify the requirements for automating administrative workflows and present an architecture and its implemented components. By using Linked Data principles to decentrally describe independent steps using states as pre- and postconditions, and composing workflows on-the-fly whilst matching a user's state to those preconditions, we automatically generate next steps to reach the user's goal. The validated solution shows its feasibility, and the upcoming interest around interoperable personal data pods (e.g., via Solid) can further increase its potential.

Within ontology engineering concepts are modeled as classes and relationships, and restrictions as axioms. Reusing ontologies requires assessing if existing ontologies are suited for an application scenario. Different scenarios not only influence concept modeling, but also the use of different restriction types, such as subclass relationships or disjointness between concepts. However, metadata about the use of such restriction types is currently unavailable, preventing accurate assessments for reuse. We created the RDF Data Cube-based dataset MontoloStats, which contains restriction use statistics for 660 LOV and 565 BioPortal ontologies. We analyze the dataset and discuss the findings and their implications for ontology reuse. The MontoloStats dataset reveals that 94% of LOV and 95% of BioPortal ontologies use RDFS-based restriction types, 49% of LOV and 52% of BioPortal ontologies use at least one OWL-based restriction type, and different literal value-related restriction types are not or barely used. Our dataset provides modeling insights, beneficial for ontology reuse to discover and compare reuse candidates, but can also be the basis of new research that investigates novel ontology engineering methodologies with respect to restrictions definition.

The process of extracting, structuring, and organizing knowledge requires processing large and originally heterogeneous data sources. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on how to semantically annotate data, and generate and publish Linked Data. We introduce [R2]RML languages to generate Linked Data. We also show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish Linked Data on their own.