Pushing the Limits of Instance Matching Systems: A Semantics-Aware Benchmark for Linked Data

The architectural choices behind the Data Web have led to the publication of large interrelated data sets that contain different descriptions for the same real-world objects. Due to the mere size of current online datasets, such duplicate instances are most commonly detected (semi-)automatically using instance matching frameworks. Choosing the right framework for this purpose remains tedious, as current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this poster, we present the Semantic Publishing Instance Matching Benchmark (SPIMBENCH) which allows the benchmarking of instance matching systems against not only structure-based and value-based test cases, but also against semantics-aware test cases based on OWL axioms. SPIMBENCH features a scalable data generator and a weighted gold standard that can be used for debugging instance matching systems and for reporting how well they perform in various matching tasks.