Module extraction - the task of computing a (preferably small) fragment M of
an ontology T that preserves entailments over a signature S - has found many
applications in recent years. Extracting modules of minimal size is, however,
computationally hard, and often algorithmically infeasible. Thus, practical
techniques are based on approximations, where M provably captures the relevant
entailments, but is not guaranteed to be minimal. Existing approximations,
however, ensure that M preserves all second-order entailments of T w.r.t. S,
which is stronger than is required in many applications, and may lead to large
modules in practice. In this paper we propose a novel approach in which module
extraction is reduced to a reasoning problem in datalog. Our approach not only
generalises existing approximations in an elegant way, but it can also be
tailored to preserve only specific kinds of entailments, which allows us to
extract significantly smaller modules. An evaluation on widely-used ontologies
has shown very encouraging results.

本文介绍了一种新的模块抽取方法，将模块抽取的问题转化为 Datalog 推理问题，该方法不仅可以优雅地概括现有的近似方法，而且可以根据需要保留特定类型的导出结果，从而提取更小的模块。