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RADAR paper published on Semantic Web Journal
The paper "A RADAR for Information Reconciliation in Question Answering Systems over Linked Data" authored by Elena Cabrio, Serena Villata and Alessio Palmero Aprosio, is now published on the Semantic Web Journal (IF 1.786).
In the latest years, more and more structured data is published on the Web and the need to support typical Web users to access this body of information has become of crucial importance. Question Answering systems over Linked Data try to address this need by allowing users to query Linked Data using natural language. These systems may query at the same time different heterogenous interlinked datasets, that may provide different results for the same query. The obtained results can be related by a wide range of heterogenous relations, e.g., one can be the specification of the other, an acronym of the other, etc. In other cases, such results can contain an inconsistent set of information about the same topic. A well known example of such heterogenous interlinked datasets are language-specific DBpedia chapters, where the same information may be reported in different languages. Given the growing importance of multilingualism in the Semantic Web community, and in Question Answering over Linked Data in particular, we choose to apply information reconciliation to this scenario. In this paper, we address the issue of reconciling information obtained by querying the SPARQL endpoints of language-specific DBpedia chapters. Starting from a categorization of the possible relations among the resulting instances, we provide a framework to: (i) classify such relations, (ii) reconcile information using argumentation theory, (iii) rank the alternative results depending on the confidence of the source in case of inconsistencies, and (iv) explain the reasons underlying the proposed ranking. We release the resource obtained applying our framework to a set of language-specific DBpedia chapters, and we integrate such framework in the Question Answering system QAKiS, that exploits such chapters as RDF datasets to be queried using a natural language interface.