This article first distinguishes constraints from rules, and descriptive constraints from prescriptive ones. Both kinds can be used to calculate a constraint-based completeness (as opposed to a real-world-based completeness), i.e. evaluating how much of a knowledge base is complete with respect to some constraints, e.g. for evaluating how well this base follows given ontology design patterns or best practices. Such evaluations may also guide knowledge elicitation and modelisation. This article explores the ways constraints can be represented via relations between classes, hence via any knowledge representation language (KRL) that has an expressiveness at least equal to RDF or RDFS. Compared to the popular practice of both representing and checking constraints via queries, this approach is as simple, offers more possibilities for exploiting both knowledge and constraints, and permits the selection and use of inference engines adapted to the expressiveness of the exploited knowledge instead of the use of restricted or ad hoc constraint-validation tools. This approach is also modular in the sense it separates content from usage: the represented "content focused constraints" can then be exploited via few "content independent" queries, one for each usage and kind of constraint. This approach provides more possibilities.