Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases

Meghyn Bienvenu 1 Camille Bourgaux 2 François Goasdoué 3
1 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : We consider the problem of query-driven repairing of inconsistent DL-Lite knowledge bases: query answers are computed under inconsistency-tolerant semantics, and the user provides feedback about which answers are erroneous or missing. The aim is to find a set of ABox modifications (deletions and additions), called a repair plan, that addresses as many of the defects as possible. After formalizing this problem and introducing different notions of optimality, we investigate the computational complexity of reasoning about optimal repair plans and propose interactive algorithms for computing such plans. For deletion-only repair plans, we also present a prototype implementation of the core components of the algorithm.
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Communication dans un congrès
IJCAI: International Joint Conference on Artificial Intelligence, Jul 2016, New York, United States. 25th International Joint Conference on Artificial Intelligence, 2016, 〈http://ijcai-16.org/〉
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Meghyn Bienvenu, Camille Bourgaux, François Goasdoué. Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases. IJCAI: International Joint Conference on Artificial Intelligence, Jul 2016, New York, United States. 25th International Joint Conference on Artificial Intelligence, 2016, 〈http://ijcai-16.org/〉. 〈lirmm-01367864〉

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