Auflistung Künstliche Intelligenz 34(3) - September 2020 nach Erscheinungsdatum
1 - 10 von 19
Treffer pro Seite
Sortieroptionen
- 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.
- ZeitschriftenartikelQuerying Rich Ontologies by Exploiting the Structure of Data(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Bajraktari, LabinotOntology-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.
- 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.
- ZeitschriftenartikelRole-Value Maps and General Concept Inclusions in the Minimal Description Logic with Value Restrictions or Revisiting Old Skeletons in the DL Cupboard(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Baader, Franz; Théron, ClémentWe investigate the impact that general concept inclusions and role-value maps have on the complexity and decidability of reasoning in the description logic $$\mathcal{FL}_0$$ FL 0 . On the one hand, we give a more direct proof for ExpTime-hardness of subsumption w.r.t. general concept inclusions in $$\mathcal{FL}_0$$ FL 0 . On the other hand, we determine restrictions on role-value maps that ensure decidability of subsumption, but we also show undecidability for the cases where these restrictions are not satisfied.
- ZeitschriftenartikelOntologies for the Virtual Materials Marketplace(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Horsch, Martin Thomas; Chiacchiera, Silvia; Seaton, Michael A.; Todorov, Ilian T.; Šindelka, Karel; Lísal, Martin; Andreon, Barbara; Bayro Kaiser, Esteban; Mogni, Gabriele; Goldbeck, Gerhard; Kunze, Ralf; Summer, Georg; Fiseni, Andreas; Brüning, Hauke; Schiffels, Peter; Cavalcanti, Welchy LeiteThe Virtual Materials Marketplace (VIMMP) project, which develops an open platform for providing and accessing services related to materials modelling, is presented with a focus on its ontology development and data technology aspects. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace frontend.
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Ludwig, Bernd
- 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.
- 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.
- 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.