Results obtained querying language-specic DBpedia chap- ters SPARQL endpoints for the same query can be related by several het- erogenous relations, or contain an inconsistent set of information about the same topic. To overcome this issue in question answering systems over language-specic DBpedia chapters, we propose the RADAR frame- work for information reconciliation. Starting from a categorization of the possible relations among the resulting instances, such framework: (i) classies such relations, (ii) reconciles the obtained information using argumentation theory, (iii) ranks the alternative results depending on the condence of the source in case of inconsistencies, and (iv) explains the reasons underlying the proposed ranking.