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Auflistung Künstliche Intelligenz 34(3) - September 2020 nach Erscheinungsdatum
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- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020)
- Zeitschriftenartikelcrowd: A Visual Tool for Involving Stakeholders into Ontology Engineering Tasks(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Braun, Germán; Gimenez, Christian; Cecchi, Laura; Fillottrani, PabloWe present crowd , a web-based visual tool for ontology engineering tasks. Its aim is to involve ontology developers and domain experts into a collaborative comprehension and design of conceptual models, enhancing the communication between them and assessing their quality by fully integrating automatic reasoning in the tool. In this paper we briefly describe the initial requirements, architecture and user interface, and make an evaluation based on a use case and a comparison with related tools.
- ZeitschriftenartikelUsing Feature-Based Description Logics to avoid Duplicate Elimination in Object-Relational Query Languages(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Toman, David; Weddell, GrantA sound inference procedure is presented for removing operations that eliminate duplicates in queries formulated in a bag-algebra. The procedure is shown complete for positive queries over finite databases, and operates by appeal to logical consequence problems for feature-based description logics in which a TBox embeds an object-relational schema. For unions of conjunctive queries in which an embedded schema excludes cover constraints, the procedure runs in PTIME, and in EXPTIME otherwise.
- ZeitschriftenartikelQuantitative Variants of Language Equations and their Applications to Description Logics(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Marantidis, PavlosUnification in description logics (DLs) has been introduced as a novel inference service that can be used to detect redundancies in ontologies, by finding different concepts that may potentially stand for the same intuitive notion. Together with the special case of matching, they were first investigated in detail for the DL $${\mathcal{FL}}_0$$ FL 0 , where these problems can be reduced to solving certain language equations. In this thesis, we extend this service in two directions. In order to increase the recall of this method for finding redundancies, we introduce and investigate the notion of approximate unification, which basically finds pairs of concepts that “almost” unify, in order to account for potential small modelling errors. The meaning of “almost” is formalized using distance measures between concepts. We show that approximate unification in $${\mathcal{FL}}_0$$ FL 0 can be reduced to approximately solving language equations, and devise algorithms for solving the latter problem for particular distance measures. Furthermore, we make a first step towards integrating background knowledge, formulated in so-called TBoxes, by investigating the special case of matching in the presence of TBoxes of different forms. We acquire a tight complexity bound for the general case, while we prove that the problem becomes easier in a restricted setting. To achieve these bounds, we take advantage of an equivalence characterization of $${\mathcal{FL}}_0$$ FL 0 concepts that is based on formal languages. Even though our results on the approximate setting cannot deal with TBoxes yet, we prepare the framework that future research can build on.
- ZeitschriftenartikelLearning Description Logic Ontologies: Five Approaches. Where Do They Stand?(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Ozaki, AnaThe quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.
- ZeitschriftenartikelSpecial Issue on Ontologies and Data Management: Part I(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Schneider, Thomas; Šimkus, Mantas
- ZeitschriftenartikelTowards Higher-order OWL(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Homola, Martin; Kľuka, Ján; Hozzová, Petra; Svátek, Vojtěch; Vacura, MiroslavWe summarize our ongoing endeavour towards proposing a suitable higher-order description logic that could serve as the semantic foundation for higher-order OWL, similarly to $$\mathcal {SROIQ}$$ SROIQ serving as the semantic foundation of regular OWL.
- ZeitschriftenartikelInterview with Uli Sattler(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Sattler, Uli; Schneider, Thomas
- ZeitschriftenartikelSATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Manthey, Norbert; Peñaloza, Rafael; Rudolph, SebastianOne 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.
- ZeitschriftenartikelLETHE: Forgetting and Uniform Interpolation for Expressive Description Logics(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Koopmann, PatrickUniform 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.