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ThinkMind // PATTERNS 2021, The Thirteenth International Conference on Pervasive Patterns and Applications // View article patterns_2021_1_20_78002


Pattern-Based Ontological Transformations for RDF Data using SPARQL

Authors:
Marek Suchánek
Robert Pergl

Keywords: ontology, transformation, RDF, OWL, versatility, evolvability

Abstract:
RDF data are being described by ontologies (OWL or RDFS) to state the meaning and promote interoperability or so-called machine-readability. However, there are many overlapping ontologies that one can use for a single dataset. To overcome this issue, mappings between ontologies are made to capture the relations forming the overlaps. Such mappings can be used with inference and reasoning tools, but rewriting rules must be applied to transform the dataset. This work proposes a new way of transformations builds on top of the SPARQL query language. It uses defined RDF patterns representing modules that can be interrelated. Our method's primary focus is for larger-scale transformations where existing methods require hard-to-maintain, i.e., non-evolvable mapping definitions. A brief demonstration, as well as a comparison with other transformation languages, is provided.

Pages: 11 to 16

Copyright: Copyright (c) IARIA, 2021

Publication date: April 18, 2021

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-61208-850-1

Location: Porto, Portugal

Dates: from April 18, 2021 to April 22, 2021

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