A deductive module of a knowledge base KB is a subset of 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 SRIQ ( D ) 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.

One of the factors that hinder the adoption of nonmonotonic description logics in applications is performance. Even when monotonic and nonmonotonic inferences have the same asymptotic complexity, the implementation of nonmonotonic reasoning may be significantly slower. The family of nonmonotonic logics DLN is no exception to this behavior. We address this issue by introducing two provably correct and complete optimizations for reasoning in DLN. The first optimization is a module extractor that has the purpose of focusing reasoning on a relevant subset of the knowledge base. The second, called optimistic evaluation, aims at exploiting incremental reasoning in a better way. Extensive experimental evaluation shows that the optimized DLN reasoning is often compatible with interactive query answering, thus bringing nonmonotonic description logics closer to practical applications.

This paper shows how knowledge representation and reasoning techniques can be used to support organizations in complying with the GDPR, that is, the new European data protection regulation. This work is carried out in a European H2020 project called SPECIAL. Data usage policies, the consent of data subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2 called PL (policy language); compliance checking and policy validation are reduced to subsumption checking and concept consistency checking. This work proposes a satisfactory tradeoff between the expressiveness requirements on PL posed by the modeling of the GDPR, and the scalability requirements that arise from the use cases provided by SPECIAL's industrial partners. Real-time compliance checking is achieved by means of a specialized reasoner, called PLR, that leverages knowledge compilation and structural subsumption techniques. The performance of a prototype implementation of PLR is analyzed through systematic experiments, and compared with the performance of other important reasoners. Moreover, we show how PL and PLR can be extended to support richer ontologies, by means of import-by-query techniques. We prove novel tractability and intractability results related to PL, and some negative results about the restrictions posed on ontology import.

Nonmonotonic inferences are not yet supported by Description Logic technology, although their potential usefulness is widely recognized. Lack of support to nonmonotonic reasoning is due to a number of issues related to expressiveness, computational complexity, and optimizations. This work contributes to the practical support of nonmonotonic reasoning in description logics by introducing a new semantics designed to address knowledge engineering needs. The formalism is validated through extensive comparison with the other nonmonotonic DLs, and systematic scalability tests.