Auflistung nach:
Auflistung Künstliche Intelligenz 34(3) - September 2020 nach Titel
1 - 10 von 19
Treffer pro Seite
Sortieroptionen
- ZeitschriftenartikelConnecting Knowledge to Data Through Transformations in KnowID: System Description(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Fillottrani, Pablo R.; Jamieson, Stephan; Keet, C. MariaIntelligent information systems deploy applied ontologies or logic-based conceptual data models for effective and efficient data management and to assist with decision-making. A core deliberation in the design of such systems, is how to link the knowledge to the data. We recently designed a novel knowledge-to-data architecture (KnowID) which aims to solve this critical step through a set of transformation rules rather than a mapping layer, which operate between models represented in EER notation and an enhanced relational model called the ARM. This system description zooms in on the novel tool for the core component of the transformation from the Artificial Intelligence-oriented modelling to the relational database-oriented data management. It provides an overview of the requirements, design, and implementation of the modular transformations module that straightforwardly permits extension with other components of the modular KnowID architecture.
- ZeitschriftenartikelConstructing and Extending Description Logic Ontologies using Methods of Formal Concept Analysis(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Kriegel, FrancescoMy thesis describes how methods from Formal Concept Analysis can be used for constructing and extending description logic ontologies. In particular, it is shown how concept inclusions can be axiomatized from data in the description logics $$\mathcal {E}\mathcal {L}$$ E L , $$\mathcal {M}$$ M , $$\textsf {Horn}$$ Horn - $$\mathcal {M}$$ M , and $$\textsf{Prob}\text{-}\mathcal {E}\mathcal {L}$$ Prob - E L . All proposed methods are not only sound but also complete, i.e., the result not only consists of valid concept inclusions but also entails each valid concept inclusion. Moreover, a lattice-theoretic view on the description logic $$\mathcal {E}\mathcal {L}$$ E L is provided. For instance, it is shown how upper and lower neighbors of $$\mathcal {E}\mathcal {L}$$ E L concept descriptions can be computed and further it is proven that the set of $$\mathcal {E}\mathcal {L}$$ E L concept descriptions forms a graded lattice with a non-elementary rank function.
- 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.
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Ludwig, Bernd
- ZeitschriftenartikelInterview with Uli Sattler(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Sattler, Uli; Schneider, Thomas
- 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.
- 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.
- ZeitschriftenartikelMachine Understandable Policies and GDPR Compliance Checking(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Bonatti, Piero A.; Kirrane, Sabrina; Petrova, Iliana M.; Sauro, LuigiThe European General Data Protection Regulation (GDPR) calls for technical and organizational measures to support its implementation. Towards this end, the SPECIAL H2020 project aims to provide a set of tools that can be used by data controllers and processors to automatically check if personal data processing and sharing complies with the obligations set forth in the GDPR. The primary contributions of the project include: (i) a policy language that can be used to express consent, business policies, and regulatory obligations; and (ii) two different approaches to automated compliance checking that can be used to demonstrate that data processing performed by data controllers/processors complies with consent provided by data subjects, and business processes comply with regulatory obligations set forth in the GDPR.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020)
- ZeitschriftenartikelOntologies and Data Management: A Brief Survey(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Schneider, Thomas; Šimkus, MantasInformation systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.