Morph-KGC^star: Declarative Generation of RDF-star Datasets from Heterogeneous Data

Tracking #: 3238-4452

This paper is currently under review
Authors: 
Julián Arenas-Guerrero1
Ana Iglesias-Molina
David Chaves-Fraga
Daniel Garijo
Oscar Corcho1
Anastasia Dimou

Responsible editor: 
Guest Editors Tools Systems 2022

Submission type: 
Tool/System Report
Abstract: 
RDF-star has been proposed as an extension of RDF to annotate statements with triples. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no implementation has been developed so far that implements the RML-star specification. In this work, we present Morph-KGC^star , which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGC^star by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGC^star scales better for large input datasets, but it is slower when processing multiple smaller files.
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Under Review