Imperfect RDF Databases : From Modelling to Querying

Abstract : The ever-increasing interest of RDF data on the Web has led to several and important research efforts to enrich traditional RDF data formalism for the exploitation and analysis purpose. The work of this thesis is a part of the continuation of those efforts by addressing the issue of RDF data management in presence of imperfection (untruthfulness, uncertainty, etc.). The main contributions of this dissertation are as follows. (1) We tackled the trusted RDF data model. Hence, we proposed to extend the skyline queries over trust RDF data, which consists in extracting the most interesting trusted resources according to user-defined criteria. (2) We studied via statistical methods the impact of the trust measure on the Trust-skyline set.(3) We integrated in the structure of RDF data (i.e., subject-property-object triple) a fourth element expressing a possibility measure to reflect the user opinion about the truth of a statement.To deal with possibility requirements, appropriate framework related to language is introduced, namely Pi-SPARQL, that extends SPARQL to be possibility-aware query language.Finally, we studied a new skyline operator variant to extract possibilistic RDF resources that are possibly dominated by no other resources in the sense of Pareto optimality
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Amna Abidi. Imperfect RDF Databases : From Modelling to Querying. Other [cs.OH]. ISAE-ENSMA Ecole Nationale Supérieure de Mécanique et d'Aérotechique - Poitiers; Université de Tunis, 2019. English. ⟨NNT : 2019ESMA0008⟩. ⟨tel-02171934⟩

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