In ontology-mediated query answering, an ontology (often specified using description logics, DLs) is used to enrich incomplete data with domain knowledge, which is taken into account when computing query answers. A central issue in this setting is how to proceed when the data is inconsistent with the ontology, since in classical logic an inconsistent theory entails every formula, so that inconsistency-tolerant semantics are needed to obtain meaningful answers. We consider three existing inconsistency-tolerant semantics based upon the notion of a repair, defined as an inclusion-maximal subset of the data consistent with the ontology. These three semantics can be used conjointly to identify answers with different levels of confidence: those that hold in every repair (AR semantics), those entailed by the facts common to all repairs (IAR semantics), and those holding in at least one repair (brave semantics). The thesis focuses on repair-based and cost-based semantics which take into account preference information reflecting the differing reliability of the facts and/or axioms, given either by a qualitative priority relation between conflicting facts or numerical weights.