RWTH Aachen University
Data mining is the process of identifying novel, valid, and interesting patterns in data. Many data mining problems can be solved better if they are augmented with additional background knowledge. This paper discusses a framework of adding background knowledge from linked open data to a given data mining problem in a fully automatic, unsupervised manner. It introduces the FeGeLOD framework and its latest implementation, the RapidMiner linked open data extension. The authors show the use of the approach in different problem domains and discuss current research directions.