Traditionally, FCA can be used as a tool for eliciting from data a class schema in the form of either a set of attribute implications or a concept lattice. However, such a schema does not necessarily fit the point of view of a domain expert for different reasons, e.g. noise, errors or exceptions in the data. For example, in the domain of animals, an expert may expect that the rule "mammal implies do not lay eggs" holds, while this may not be the case if the platypus is among the objects in the formal context. In this paper, we propose to bridge the possible gap between the representation model based on a concept lattice and the representation model of a domain expert. The knowledge of the expert is encoded as a set of attribute dependencies or constraints which is "aligned" with the set of implications provided by the concept lattice, leading to modifications in the original concept lattice. The method can be generalized for generating lattices satisfying constraints based on attribute dependencies and using extensional projections. This method also allows the experts to keep a trace of the changes occurring in the original lattice and the revised version, and to assess how concepts in practice are related to concepts automatically issued from data.