Speaker: Stéphane Jean, maître de conférences at Université de Poitiers, Poitiers, France

Abstract: Knowledge Bases (KBs) are at the heart of the Semantic Web. A KB is a collection of entities and facts about them represented in RDF as a set of triples (subject, predicate, object). In recent years, numerous projects in both industry and academia have been conducted to build large-scale KBs. Well-known examples of KBs include YAGO and DBPEDIA resulting from academics projects as well as the KBs designed in commercial projects such as those by Google or Walmart. These KBs find applications in numerous domains, such as semantic enrichment, machine translation, document classification, query expansion, and data integration.

Due to the lack of information about the content and schema semantics of KBs, users are often not able to correctly formulate KB queries that return the intended result. In this talk, we will present cooperative techniques developed to solve the problem of failing RDF queries i.e, queries that return an empty set of answers. These techniques aim at finding the causes of failure of a Semantic Web query as well as the successful queries that satisfy the maximum of the initial query criteria. The case where RDF data are pervaded with uncertainty is also discussed. We will show how the presented cooperative techniques can be adapted to handling this imperfection. Last, we provide a panorama of different interpretations and models of uncertainty.