Effective and fast module extraction for nonempty ABoxes

Abstract

A deductive module of a knowledge base $\mathrm{KB}$ is a subset of $\mathrm{KB}$ that preserves a specified class of consequences. Module extraction is applied in ontology design, debugging, and reasoning. The locality-based module extractors of the OWL API are less effective when the knowledge base contains facts such as ABox assertions. The competing module extractor PrisM computes smaller modules at the cost of higher computation time. In this paper, we introduce and study a novel module extraction technique, called conditional module extraction, that can be applied to satisfiable $\mathrm{SRIQ}\left(D\right)$ knowledge bases. Experimental analysis shows that conditional module extraction constitutes an appealing alternative to PrisM and to the locality-based extractors of the OWL API, when the ABox is nonempty.