Data Cleansing Consolidation with PatchR

The Linking Open Data (LOD) initiative is turning large resources of publicly available structured data from various domains into interlinked RDF(S) facts to constitute the so-called “Web of Data”. But, this Web of Data is by no means a perfect world of consistent and valid facts. Linked Data has multiple dimensions of shortcomings ranging from simple syntactical errors over logical inconsistencies to complex semantic errors and wrong facts. Multiple efforts target data quality assessment or aim to detect and to resolve such shortcomings in Linked Data datasets, such as crowdsourcing based, statistical, or heuristical approaches. These approaches rather address particular problems or datasets than to be generalizable for any kind of error. Moreover, results are published in various forms, which makes it hard to combine their results. In this paper we propose the aggregation of heterogeneous Linked Data cleansing efforts by using the Patch Request ontology [1]. This allows to include less assured outcomes in order to reach a higher coverage.