For logic-based applications where data is not curated, but generated automatically, noisy or erroneous data can clearly be an obstacle for reasoning under classical First-order semantics. In recent years several approaches have been investigated for reasoning in Description Logics that deal with this problem often by changing the underlying semantics. In this talk I will discuss diferent reasoning problems using non-standard semantics, such as defeasible or approximative semantics, that can preserve useful logical reasoning even in the presence of imperfect data.