Module extraction is the task of computing a (preferably small) fragment M of an ontology O that preserves a class of entailments over a signature of interest ∑. Existing practical approaches ensure that M preserves all second-order entailments of O over ∑, which is a stronger condition than is required in many applications. In the first part of this thesis, we propose a novel approach to module extraction which, based on a reduction to a datalog reasoning problem, makes it possible to compute modules that are tailored to preserve only specific kinds of entailments. This leads to obtaining modules that are often significantly smaller than those produced by other practical approaches, as shown in an empirical evaluation. In the second part of this thesis, we consider the application of module extraction to the optimisation of ontology classification. Classification is a fundamental reasoning task in ontology design, and there is currently a wide range of reasoners that provide this service. Reasoners aimed at so-called lightweight ontology languages are much more efficient than those aimed at more expressive ones, but they do not offer completeness guarantees for ontologies containing axioms outside the relevant language. We propose an original approach to classification based on exploiting module extraction techniques to divide the workload between a general purpose reasoner and a more efficient reasoner for a lightweight language in such a way that the bulk of the workload is assigned to the latter. We show how the proposed approach can be realised using two particular module extraction techniques, including the one presented in the first part of the thesis. Furthermore, we present the results of an empirical evaluation that shows that this approach can lead to a significant performance improvement in many cases.