KI - Künstliche Intelligenz

Permanent URI for this communityhttps://dl.gi.de/handle/20.500.12116/11025

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

Authors with most Documents  

Visser, Ubbo
21
Ragni, Marco
13
Richter, Kai-Florian
12
Sonntag, Daniel
12
Beetz, Michael
11

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Author: search.filters.author.Peñaloza, RafaelSubject: search.filters.subject.Description logics

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  • Journal Article
    Error-Tolerance and Error Management in Lightweight Description Logics
    (Springer, 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.
  • Journal Article
    SATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT
    (Springer, 2020) Manthey, Norbert; Peñaloza, Rafael; Rudolph, Sebastian
    One approach to axiom pinpointing (AP) in description logics is its reduction to the enumeration of minimal unsatisfiable subformulas, allowing for the deployment of highly optimized methods from SAT solving. Exploiting the properties of AP, we further optimize incremental SAT solving, resulting in speedups of several orders of magnitude: through persistent incremental solving the solver state is updated lazily when adding clauses or assumptions. This adaptation consistently improves the runtime of the tool by an average factor of 3.8, and a maximum of 38. SATPin , our system, was tested over large biomedical ontologies and performed competitively.