Linked Data sources on the Web use a wide range of different vocabularies to represent data describing the same type of entity. For some types of entities, like people or bibliographic record, common vocabularies have emerged that are used by multiple data sources. But even for representing data of these common types, different user communities use different competing common vocabularies. Linked Data applications that want to understand as much data from the Web as possible, thus need to overcome vocabulary heterogeneity and translate the original data into a single target vocabulary. To support application developers with this integration task, several Linked Data translation systems have been developed. These systems provide languages to express declarative mappings that are used to translate heterogeneous Web data into a single target vocabulary.
LODIB (Linked Open Data Integration Benchmark) is a benchmark for comparing the expressivity as well as the runtime performance of Linked Data translation systems. The benchmark aims to reflect the real-world heterogeneities that exist on the Web of Linked Data and has thus been designed based on statistics that were derived from the LOD Cloud.
Please send comments and feedback about the benchmark to Carlos Rivero, Andreas Schultz and Chris Bizer.