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Auflistung Künstliche Intelligenz 34(4) - Dezember 2020 nach Schlagwort "Description logics"
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- 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.
- 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.
- ZeitschriftenartikelSemantic Technologies for Situation Awareness(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Baader, Franz; Borgwardt, Stefan; Koopmann, Patrick; Thost, Veronika; Turhan, Anni-YasminThe 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.
- ZeitschriftenartikelThe AAA ABox Abduction Solver(KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Pukancová, Júlia; Homola, MartinAAA 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.