Data repair of inconsistent nonmonotonic description logic programs

Abstract

Combining Description Logic (DL) ontologies and nonmonotonic rules has gained in-<lb>creasing attention in the past decade, due to the growing range of applications of DLs. A well-<lb>known proposal for such a combination are non-monotonic DL-programs, which support rule-based<lb>reasoning on top of DL ontologies in a loose coupling, using a well-defined query interface. How-<lb>ever, inconsistency may easily arise as a result of the interaction of the rules and the ontology,<lb>such that no answer set (i.e., model) of a DL-program exists; this makes the program useless. To<lb>overcome this problem, we present a framework for repairing inconsistencies in DL-programs by<lb>exchanging formulas of an ontology formulated in DL-LiteA, which is a prominent DL that al-<lb>lows for tractable reasoning. Viewing the data part of the ontology as a source of inconsistency,<lb>we define program repairs and repair answer sets based on them. We analyze the complexity of<lb>the notion, and we extend an algorithm for evaluating DL-programs to compute repair answer sets,<lb>under optional selection of preferred repairs that satisfy additional constraints. The algorithm in-<lb>duces a generalized ontology repair problem, in which the entailment respectively non-entailment<lb>of queries to the ontology, subject to possible updates, must be achieved by a data change. While<lb>this problem is intractable in general, we identify several tractable classes of preferred repairs that<lb>are useful in practice. For the class of deletion repairs among them, we optimize the algorithm by<lb>reducing query evaluation to constraint matching, based on the novel concept of support set, which<lb>roughly speaking is a portion of the data from which entailment of an ontology query follows. Our<lb>repair approach is implemented within an answer set program system, using a declarative method<lb>for repair computation. An experimental evaluation on a suite of benchmark problems shows the<lb>effectiveness of our approach and promising results, both regarding performance and quality of the<lb>obtained repairs. While we concentrate on DL-LiteA ontologies, our notions extend to other DLs,<lb>for which more general computation approaches may be used. 1Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 Vienna, Austria;<lb>email: {eiter,fink,dasha}@kr.tuwien.ac.at. Acknowledgements: This work has been supported by the Austrian Science Fund (FWF) project P24090. Publication Information: This article is a revised and significantly extended version of the papers [33] and<lb>[35] published at IJCAI 2013 and ECAI 2014 respectively.<lb>Copyright c<lb>© 2015 by the authors 2<lb>INFSYS RR 15-03

DOI: 10.1016/j.artint.2016.06.003

Extracted Key Phrases

13 Figures and Tables

Cite this paper

@article{Eiter2016DataRO, title={Data repair of inconsistent nonmonotonic description logic programs}, author={Thomas Eiter and Michael Fink and Daria Stepanova}, journal={Artif. Intell.}, year={2016}, volume={239}, pages={7-53} }