Künstliche Intelligenz 34(4) - Dezember 2020

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  • Zeitschriftenartikel
    Rethinking Computer Science Through AI
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Kersting, Kristian
  • 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
    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
    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
    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
    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
    Ontology-Mediated Querying with Horn Description Logics
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Sabellek, Leif
    An ontology-mediated query (OMQ) consists of a database query paired with an ontology. When evaluated on a database, an OMQ returns not only the answers that are already in the database, but also those answers that can be obtained via logical reasoning using rules from ontology. There are many open questions regarding the complexities of problems related to OMQs. Motivated by the use of ontologies in practice, new reasoning problems which have never been considered in the context of ontologies become relevant, since they can improve the usability of ontology enriched systems. This thesis deals with various reasoning problems that emerge from ontology-mediated querying and it investigates the computational complexity of these problems. We focus on ontologies formulated in Horn description logics, which are a popular choice for ontologies in practice. In particular, the thesis gives results regarding the data complexity of OMQ evaluation by completely classifying complexity and rewritability questions for OMQs based on an EL ontology and a conjunctive query. Furthermore, the query-by-example problem, and the expressibility and verification problem in ontology-based data access are introduced and investigated.
  • 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
    Semantic Technologies for Situation Awareness
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Baader, Franz; Borgwardt, Stefan; Koopmann, Patrick; Thost, Veronika; Turhan, Anni-Yasmin
    The project “Semantic Technologies for Situation Awareness” was concerned with detecting certain critical situations from data obtained by observing a complex hard- and software system, in order to trigger actions that allow this system to save energy. The general idea was to formalize situations as ontology-mediated queries, but in order to express the relevant situations, both the employed ontology language and the query language had to be extended. In this paper we sketch the general approach and then concentrate on reporting the formal results obtained for reasoning in these extensions, but do not describe the application that triggered these extensions in detail.