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<title>Künstliche Intelligenz 34(4) - Dezember 2020</title>
<link href="http://dl.gi.de/handle/20.500.12116/36222" rel="alternate"/>
<subtitle/>
<id>http://dl.gi.de/handle/20.500.12116/36222</id>
<updated>2026-07-21T14:37:30Z</updated>
<dc:date>2026-07-21T14:37:30Z</dc:date>
<entry>
<title>A Lightweight Defeasible Description Logic in Depth</title>
<link href="http://dl.gi.de/handle/20.500.12116/36333" rel="alternate"/>
<author>
<name>Pensel, Maximilian</name>
</author>
<id>http://dl.gi.de/handle/20.500.12116/36333</id>
<updated>2021-04-23T09:42:44Z</updated>
<published>2020-01-01T00:00:00Z</published>
<summary type="text">A Lightweight Defeasible Description Logic in Depth
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.
</summary>
<dc:date>2020-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>The AAA ABox Abduction Solver</title>
<link href="http://dl.gi.de/handle/20.500.12116/36334" rel="alternate"/>
<author>
<name>Pukancová, Júlia</name>
</author>
<author>
<name>Homola, Martin</name>
</author>
<id>http://dl.gi.de/handle/20.500.12116/36334</id>
<updated>2021-04-23T09:42:44Z</updated>
<published>2020-01-01T00:00:00Z</published>
<summary type="text">The AAA ABox Abduction Solver
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.
</summary>
<dc:date>2020-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Special Issue on Ontologies and Data Management: Part II</title>
<link href="http://dl.gi.de/handle/20.500.12116/36332" rel="alternate"/>
<author>
<name>Schneider, Thomas</name>
</author>
<author>
<name>Šimkus, Mantas</name>
</author>
<id>http://dl.gi.de/handle/20.500.12116/36332</id>
<updated>2021-04-23T09:42:44Z</updated>
<published>2020-01-01T00:00:00Z</published>
<summary type="text">Special Issue on Ontologies and Data Management: Part II
Schneider, Thomas; Šimkus, Mantas
</summary>
<dc:date>2020-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Defeasible Description Logics</title>
<link href="http://dl.gi.de/handle/20.500.12116/36335" rel="alternate"/>
<author>
<name>Varzinczak, Ivan</name>
</author>
<id>http://dl.gi.de/handle/20.500.12116/36335</id>
<updated>2021-04-23T09:42:44Z</updated>
<published>2020-01-01T00:00:00Z</published>
<summary type="text">Defeasible Description Logics
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.
</summary>
<dc:date>2020-01-01T00:00:00Z</dc:date>
</entry>
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