Logo des Repositoriums

Künstliche Intelligenz 34(4) - Dezember 2020

Autor*innen mit den meisten Dokumenten  

Auflistung nach:

Neueste Veröffentlichungen

1 - 10 von 21
  • Zeitschriftenartikel
    Defeasible Description Logics
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Varzinczak, Ivan
    The 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.
  • Zeitschriftenartikel
    A Lightweight Defeasible Description Logic in Depth
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Pensel, Maximilian
    In 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.
  • Zeitschriftenartikel
    Special Issue on Ontologies and Data Management: Part II
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Schneider, Thomas; Šimkus, Mantas
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020)
  • Zeitschriftenartikel
    Error-Tolerance and Error Management in Lightweight Description Logics
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Peñaloza, Rafael
    The 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.
  • Zeitschriftenartikel
    The AAA ABox Abduction Solver
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Pukancová, Júlia; Homola, Martin
    AAA is a sound and complete ABox abduction solver based on the Reiter’s MHS algorithm and the Pellet reasoner. It supports DL expressivity up to $$\mathcal {SROIQ}$$ SROIQ (i.e., OWL 2). It supports multiple observations, and allows to specify abducibles.
  • Zeitschriftenartikel
    Rethinking Computer Science Through AI
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Kersting, Kristian
  • Zeitschriftenartikel
    onto2problog: A Probabilistic Ontology-Mediated Querying System using Probabilistic Logic Programming
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Bremen, Timothy; Dries, Anton; Jung, Jean Christoph
    We present onto2problog , a tool that supports ontology-mediated querying of probabilistic data via probabilistic logic programming engines. Our tool supports conjunctive queries on probabilistic data under ontologies encoded in the description logic $$\mathcal{ELH}^{dr}$$ ELH dr , thus capturing a large part of the OWL 2 EL profile.
  • Zeitschriftenartikel
    All-Instances Restricted Chase Termination for Linear TGDs
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Gogacz, Tomasz; Marcinkowski, Jerzy; Pieris, Andreas
    The 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.
  • Zeitschriftenartikel
    NoHR: An Overview
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Kasalica, Vedran; Knorr, Matthias; Leite, João; Lopes, Carlos
    Description 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.