RML Mapper: a tool for uniform Linked Data generation from heterogeneous data

Tracking #: 1730-2942

This paper is currently under review
Anastasia Dimou
Ben De Meester
Pieter Heyvaert
Ruben Verborgh
Steven Latré
Erik Mannens

Responsible editor: 
Aidan Hogan

Submission type: 
Tool/System Report
Linked Data is often considered a means of integration among data residing in different data sources. In real-world situations though, data sources of various formats are accessed using different protocols and contain data in various structures, formats and serializations. Generating Linked Data from these data sources remains complicated, despite the significant number of existing tools, because, the latter provide their own –thus not interoperable– format- and source-specific approaches. Linked Data generation is facilitated by mapping languages which detach the rules from the implementation that executes them, rendering them interoperable among different solutions, whilst systems that process those rules are use-case independent. Nevertheless, different factors influence the Linked Data generation process. Thus, diverse systems may be implemented to efficiently execute those mapping rules. In this paper, we present the RMLMapper, a tool for Linked Data generation from data with heterogeneous structure, format and serialization, which is retrieved from different data sources with various access interfaces. We (i) introduce the RMLM apper’s design choices and architecture, and (ii) demonstrate evaluation results and use cases where the RMLMapper was adopted, showing that the RMLM apper is well-adopted and capable of generating Linked Data in competitive time.
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