RWTH Aachen University
Today, data integration results produced by various developed automated algorithms and systems are still error-prone. Ontology matching approaches have mostly worked on the schema level so far. With the advent of linked open data and the availability of a massive amount of instance information, instance-based approaches become possible. This position paper discusses approaches and challenges for using those instances as input for machine learning algorithms, with a focus on rule learning algorithms, as a means for ontology matching.