Declarative Data Transformations for Linked Data Generation: The Case of DBpedia

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)


Mapping languages allow us to define how Linked Data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked Data. Since complex data transformations remain out of scope for mapping languages, these steps are often implemented as custom solutions, or with systems separate from the mapping process. The former data transformations remain case-specific, often coupled with the mapping, whereas the latter are not reusable across systems. In this paper, we propose an approach where data transformations (i) are defined declaratively and (ii) are aligned with the mapping languages. We employ an alignment of data transformations described using the Function Ontology ( Open image in new window ) and mapping of data to Linked Data described using the rdf Mapping Language (rml). We validate that our approach can map and transform dbpedia in a declaratively defined and aligned way. Our approach is not case-specific: data transformations are independent of their implementation and thus interoperable, while the functions are decoupled and reusable. This allows developers to improve the generation framework, whilst contributors can focus on the actual Linked Data, as there are no more dependencies, neither between the transformations and the generation framework nor their implementations.


Data transformations FnO Linked Data generation RML 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ghent University - imec - IDLabGhentBelgium

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