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
2 LRI - Laboratoire de Recherche en Informatique
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement

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.

Domaines: Intelligence artificielle [cs.AI]

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. ⟨lirmm-01367864⟩