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Auflistung Künstliche Intelligenz 34(4) - Dezember 2020 nach Titel
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- ZeitschriftenartikelA Lightweight Defeasible Description Logic in Depth(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Pensel, MaximilianIn this thesis we study KLM-style rational reasoning in defeasible Description Logics. We illustrate that many recent approaches to derive consequences under Rational Closure (and its stronger variants, lexicographic and relevant closure) suffer the fatal drawback of neglecting defeasible information in quantified concepts. We propose novel model-theoretic semantics that are able to derive the missing entailments in two differently strong flavours. Our solution introduces a preference relation to distinguish sets of models in terms of their typicality (amount of defeasible information derivable for quantified concepts). The semantics defined through the most typical (most preferred) sets of models are proven superior to previous approaches in that their entailments properly extend previously derivable consequences, in particular, allowing to derive defeasible consequences for quantified concepts. The dissertation concludes with an algorithmic characterisation of this uniform maximisation of typicality, which accompanies our investigation of the computational complexity for deriving consequences under these new semantics.
- ZeitschriftenartikelA Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Bienvenu, MeghynThis paper provides a concise overview of the literature on inconsistency handling for ontology-mediated query answering, a topic which has grown into an active area of research over the last decade. The focus of this survey is on the case where errors are localized in the data (i.e., the ontology is deemed reliable) and where inconsistency-tolerant semantics are employed with the aim of obtaining meaningful information from inconsistent knowledge bases.
- ZeitschriftenartikelAll-Instances Restricted Chase Termination for Linear TGDs(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Gogacz, Tomasz; Marcinkowski, Jerzy; Pieris, AndreasThe chase procedure is a fundamental algorithmic tool in database theory with a variety of applications. A key problem concerning the chase procedure is all-instances chase termination: for a given set of tuple-generating dependencies (TGDs), is it the case that the chase terminates for every input database? In view of the fact that this problem is, in general, undecidable, it is natural to ask whether well-behaved classes of TGDs, introduced in different contexts, ensure decidability. It has been recently shown that the problem is decidable for the restricted (a.k.a. standard) version of the chase, and linear TGDs, a prominent class of TGDs that has been introduced in the context of ontological query answering, under the assumption that only one atom appears in TGD-heads. We provide an alternative proof for this result based on Monadic Second-Order Logic, which we believe is simpler that the ones obtained from the literature.
- ZeitschriftenartikelData Access With Horn Ontologies: Where Description Logics Meet Existential Rules(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Mugnier, Marie-LaureTwo main families of ontology languages are considered in the context of data access, namely Horn description logics and existential rules. In this paper, we review the semantic relationships between these families in the light of the ontology-mediated query answering problem. To this end, we rely on the standard translation of description logics in first-order logic and on the notion of semantic emulation. We focus on description logics and classes of existential rules for which the conjunctive query answering problem has polynomial data complexity.
- ZeitschriftenartikelDefeasible Description Logics(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Varzinczak, IvanThe present paper is a summary of a habilitation ( Habilitation à Diriger des Recherches , in French), which has been perused and evaluated by a committee composed by the following members: Franz Baader, Stéphane Demri, Hans van Ditmarsch, Sébastien Konieczny, Pierre Marquis, Marie-Laure Mugnier, Odile Papini and Leon van der Torre. It was defended on 26 November 2019 at Université d’Artois in Lens, France.
- ZeitschriftenartikelError-Tolerance and Error Management in Lightweight Description Logics(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Peñaloza, RafaelThe construction and maintenance of ontologies is an error-prone task. As such, it is not uncommon to detect unwanted or erroneous consequences in large-scale ontologies which are already deployed in production. While waiting for a corrected version, these ontologies should still be available for use in a “safe” manner, which avoids the known errors. At the same time, the knowledge engineer in charge of producing the new version requires support to explore only the potentially problematic axioms, and reduce the number of exploration steps. In this paper, we explore the problem of deriving meaningful consequences from ontologies which contain known errors. Our work extends the ideas from inconsistency-tolerant reasoning to allow for arbitrary entailments as errors, and allows for any part of the ontology (be it the terminological elements or the facts) to be the causes of the error. Our study shows that, with a few exceptions, tasks related to this kind of reasoning are intractable in general, even for very inexpressive description logics.
- ZeitschriftenartikelIn Memoriam Christian Freksa(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Piel, Helen; Seising, Rudolf
- ZeitschriftenartikelInterview with Diego Calvanese(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Calvanese, Diego; Šimkus, Mantas
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020)
- ZeitschriftenartikelNoHR: An Overview(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Kasalica, Vedran; Knorr, Matthias; Leite, João; Lopes, CarlosDescription logic ontologies, such as ontologies written in OWL, and non-monotonic rules, as known in Logic Programming, are two major approaches in Knowledge Representation and Reasoning. Even though their integration is challenging due to their inherent differences, the need to combine their distinctive features stems from real world applications. In this paper, we give an overview of NoHR, a reasoner designed to answer queries over theories composed of an OWL ontology in a Description logic and a set of non-monotonic rules. NoHR has been developed as a plug-in for the widely used ontology editor Protégé, building on a combination of reasoners dedicated to OWL and rules, but it is also available as a library, allowing for its integration within other environments and applications. It comes with support for all polynomial OWL profiles and the integration of their constructors as well as for standard built-in Prolog predicates, and allows the direct consultation of databases during query evaluation and the usage of sophisticated mechanisms, such as tabling already computed results, all of which enhances the applicability and the efficiency of query answering.
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