Künstliche Intelligenz 34(3) - September 2020

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  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020)
  • Zeitschriftenartikel
    Special Issue on Ontologies and Data Management: Part I
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Schneider, Thomas; Šimkus, Mantas
  • Zeitschriftenartikel
    Quantitative Variants of Language Equations and their Applications to Description Logics
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Marantidis, Pavlos
    Unification 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.
  • Zeitschriftenartikel
    crowd: 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, Pablo
    We 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.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Ludwig, Bernd
  • Zeitschriftenartikel
    Constructing and Extending Description Logic Ontologies using Methods of Formal Concept Analysis
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Kriegel, Francesco
    My 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.
  • 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
    Machine Understandable Policies and GDPR Compliance Checking
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Bonatti, Piero A.; Kirrane, Sabrina; Petrova, Iliana M.; Sauro, Luigi
    The 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.
  • Zeitschriftenartikel
    Reasoning in Description Logic Ontologies for Privacy Management
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Nuradiansyah, Adrian
    This work is initially motivated by a privacy scenario in which the confidential information about persons or its properties formulated in description logic (DL) ontologies should be kept hidden. We investigate procedures to detect whether this confidential information can be disclosed in a certain situation by using DL formalisms. If it is the case that this information can be deduced from the ontologies, which implies certain privacy policies are not fulfilled, then one needs to consider methods to repair these ontologies in a minimal way such that the modified ontologies complies with the policies. However, privacy compliance itself is not enough if a possible attacker can also obtain relevant information from other sources, which together with the modified ontologies might violate the privacy policy. This article provides a summary of studies and results from Adrian Nuradiansyah’s Ph.D. dissertation that are corresponding to the addressed problem above with a special emphasis on the investigations on the worst-case complexities of those problems as well as the complexity of the procedures and algorithms solving the problems.
  • Zeitschriftenartikel
    Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?
    (KI - Künstliche Intelligenz: Vol. 34, No. 3, 2020) Ozaki, Ana
    The 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.