Completeness and optimality in ontology alignment debugging

The benefit of light-weight reasoning in ontology matching has been recognized by a number of researchers resulting in alignment repair systems such as Alcomo and LogMap. While the general benefit of logical reasoning has been shown in principle, there is no systematic empirical evaluation analyzing (i) the impact of completeness of the reasoning methods and (ii) whether approximate or optimal solutions to the conflict resolution problem have to be preferred. Using standard benchmark data sets, we show that increasing the expressive power does improve the matching results and that optimal resolution methods slightly outperform approximate ones.