RDF graphs are commonly derived from (semi-) structured data by applying a set of
mappings. RDF graph construction is performed by either materialising or virtualising an …

The availability of data sources during the Big Data era provides the opportunity for new
analytical applications in the networking domain, which are envisioned as one of the main …

RDF-star has been proposed as an extension of RDF to make statements about statements.
Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star …

The significant increase in data volume in recent years has prompted the adoption of
knowledge graphs as valuable data structures for integrating diverse data and metadata …

Abstract With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously
grow in numbers, complexity and components. Online platforms (eg, OpenML, Kaggle) aim …

Reification in knowledge graphs has been present since the inception of RDF to allow
capturing additional information in triples, usually metadata. The need of adopting or …

Creating SHACL shapes for the validation of RDF graphs is a non-trivial endeavor.
Automated shape extraction systems typically derive SHACL shapes from RDF graphs, and …

The RML mapping language declares schema transformations to map heterogeneous data
into knowledge graphs. Although the schema transformations provided by RML are sufficient …

This paper explores using large language models (LLMs) to generate RDF mapping
language (RML) files in the RDF turtle format as a key step towards self-configuring RDF …

FAIR data presupposes their successful communication between machines and humans
while preserving their meaning and reference, requiring all parties involved to share the …