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R2R 0.2

OS Support:

Windows XP/2000/98


Publisher Old version


August 25 2010


R2R 0.2


Translating RDF data from the Web to a target vocabulary.

BS Editor: The promise of the Web of Linked Data is to enable client applications to discover new data sources by following RDF links at run-time and to smoothly integrate data from these sources. Linked Data sources use different vocabularies to describe the same type of objects. It is also common practice to mix terms from different widely used vocabularies with proprietary terms. In contrast, Linked Data applications usually expect data to be represented using a consistent target vocabulary. Thus Linked Data applications need to translate Web data to their local schema before doing any sophisticated data processing. The R2R Framework supports them with this. The framework consists of a mapping language for expressing term correspondences, best-practices on how to publish mappings on the Web and a Java API for transforming data according to these mappings. The syntax of the R2R mapping language is very Similar to the query language SPARQL, which eases the learning curve. The mapping language covers value transformation for use cases where RDF datasets use different units of measurement and can handle one-to-many and many-to-one correspondences between vocabulary elements. The R2R Java API transforms Web data to a given target vocabulary.

It support them by:

1. providing the R2R Mapping Language for publishing fine-grained term mappings on the Web
2. defining best-practices on how mappings can be discovered by Linked Data applications
3. providing an open-source implementation of the R2R Mapping Engine.

This document gives a short overview of the R2R Framework, describes its installation and configuration and gives several mapping examples.


* 2010-07-30: Version 0.2 released. Composition method for chaining partial mappings from different sources based on a mapping quality assessment heuristic added.

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