Joint proceedings of Second International Workshop on Semantic Web Enterprise Adoption and Best Practice ( WaSABi 2014 ) & Second International Workshop on Finance and Economics

Over the past years, a vast number of datasets have been published based on Semantic Web standards, which provides an opportunity for creating novel industrial applications. However, industrial requirements on data quality are high while the time to market as well as the required costs for data preparation have to be kept low. Unfortunately, many Linked Data sources are error-prone which prevents their direct use in productive systems. Hence, (semi-)automatic quality assurance processes are needed as manual ontology repair procedures by domain experts are expensive and time consuming. In this article, we present CROCUS – a pipeline for cluster-based ontology data cleansing. Our system provides a semi-automatic approach for instance-level error detection in ontologies which is agnostic of the underlying Linked Data knowledge base and works at very low costs. CROCUS was evaluated on two datasets. The experiments show that we are able to detect errors with high recall.