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Auflistung nach Schlagwort "Description logics"

1 - 10 von 12
  • Zeitschriftenartikel
    A Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering
    (KI - Künstliche Intelligenz: Vol. 34, No. 4, 2020) Bienvenu, Meghyn
    This 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.
  • Zeitschriftenartikel
    Abductive Conjunctive Query Answering w.r.t. Ontologies
    (KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Möller, Ralf; Özçep, Özgür; Haarslev, Volker; Nafissi, Anahita; Wessel, Michael
    In this article we investigate abductive conjunctive query answering w.r.t. ontologies and show how use cases can benefit from this kind of query answering service. While practical reasoning systems such as Racer have supported abductive conjunctive query answering for 10 years now, and many projects have exploited this feature, few publications deal with A-box abduction from an implementation perspective. This article gives a generalized overview on features provided by practical systems and also explains optimization techniques needed to meet practical requirements.
  • 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
    LETHE: Forgetting and Uniform Interpolation for Expressive Description Logics
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Koopmann, Patrick
    Uniform interpolation and forgetting describe the task of projecting a given ontology into a user-specified vocabulary, that is, of computing a new ontology that only uses names from a specified set of names, while preserving all logical entailments that can be expressed with those names. This is useful for ontology analysis, ontology reuse and privacy. Lethe  is a tool for performing uniform interpolation on ontologies in expressive description logics, and it can be used from the command line, using a graphical interface, and as a Java library. It furthermore implements methods for computing logical difference and performing abduction using uniform interpolation. We present the tool together with an evaluation on a varied corpus of realistic ontologies.
  • Zeitschriftenartikel
    Many Facets of Reasoning Under Uncertainty, Inconsistency, Vagueness, and Preferences: A Brief Survey
    (KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Kern-Isberner, Gabriele; Lukasiewicz, Thomas
    In this paper, we give an introduction to reasoning under uncertainty, inconsistency, vagueness, and preferences in artificial intelligence (AI), including some historic notes and a brief survey to previous approaches.
  • Zeitschriftenartikel
    Quantitative Methods for Similarity in Description Logics
    (KI - Künstliche Intelligenz: Vol. 31, No. 1, 2017) Ecke, Andreas
    Description logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in a formally well-defined way. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. In practice though, knowledge is rarely exact. Many definitions have exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are “close enough”. We are interested in tackling how to express that something is “close enough”, and how to integrate this notion into the formalism of DLs. To this end we employ the notion of similarity and dissimilarity measures, we will look at how useful measures can be defined in the context of DLs and two particular applications: Relaxed instance queries will use a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are “close enough”.
  • Zeitschriftenartikel
    Querying Rich Ontologies by Exploiting the Structure of Data
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Bajraktari, Labinot
    Ontology-based data access (OBDA) has emerged as a paradigm for accessing heterogeneous and incomplete data sources. A fundamental reasoning service in OBDA, the ontology mediated query (OMQ) answering has received much attention from the research community. However, there exists a disparity in research carried for OMQ algorithms for lightweight DLs which have found their way into practical implementations, and algorithms for expressive DLs for which the work has had mainly theoretical oriented goals. In the dissertation, a technique that leverages the structural properties of data to help alleviate the problems that typically arise when answering the queries in expressive settings is developed. In this paper, a brief summary of the technique along with the different algorithms developed for OMQ for expressive DLs is given.
  • Zeitschriftenartikel
    SATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 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.
  • 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.
  • Zeitschriftenartikel
    Small is Again Beautiful in Description Logics
    (KI - Künstliche Intelligenz: Vol. 24, No. 1, 2010) Baader, Franz; Lutz, Carsten; Turhan, Anni-Yasmin
    The Description Logic (DL) research of the last 20 years was mainly concerned with increasing the expressive power of the employed description language without losing the ability of implementing highly-optimized reasoning systems that behave well in practice, in spite of the ever increasing worst-case complexity of the underlying inference problems. OWL DL, the standard ontology language for the Semantic Web, is based on such an expressive DL for which reasoning is highly intractable. Its sublanguage OWL Lite was intended to provide a tractable version of OWL, but turned out to be only of a slightly lower worst-case complexity than OWL DL. This and other reasons have led to the development of two new families of light-weight DLs, $\mathcal{EL}$ and DL-Lite, which recently have been proposed as profiles of OWL 2, the new version of the OWL standard. In this paper, we give an introduction to these new logics, explaining the rationales behind their design.