The R2R Framework: Publishing and Discovering Mappings on the Web

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. Thus Linked Data applications need to apply mappings to translate Web data to their local schema before doing any sophisticated data processing. Maintaining a local or central set of mappings that cover all Linked Data sources is likely to be impossible due to the size and dynamics of the Web of Linked Data. Thus this paper propagates a distributed, pay-as-you-go integration approach where data publishers, vocabulary maintainers and third parties may publish expressive mappings on the Web. A client application which discovers data that is represented using terms that are unknown to the application may search the Web for mappings and apply the discovered mappings to translate data to its local schema. We propose a language for publishing expressive, named mappings on the Web and a composition method for chaining partial mappings from different sources based on a mapping quality assessment heuristic. The composition method is implemented within the R2R Mapping Engine which can be used by Linked Data applications to translate Web data to their local schema.