Terminological knowledge aquisition in probalistic description logic

For a probabilistic extension of the description logic EL⊥, we consider the task of automatic acquisition of terminological knowledge from a given probabilistic interpretation. Basically, such a probabilistic interpretation is a family of directed graphs the vertices and edges of which are labeled, and where a discrete probabilitymeasure on this graph family is present. The goal is to derive so-called concept inclusions which are expressible in the considered probabilistic description logic and which hold true in the given probabilistic interpretation. A procedure for an appropriate axiomatization of such graph families is proposed and its soundness and completeness is justified

A survey of current, stand-alone OWL Reasoners

Abstract. We present a survey of the current OWL reasoner landscape. Through literature and web search we have identified 35 OWL reasoners that are, at least to some degree, actively maintained. We conducted a survey directly addressing the respective developers, and collected 33 responses. We present an analysis of the survey, characterising all reasoners across a wide range of categories such as supported expressiveness and reasoning services. We will also provide some insight about ongoing research efforts and a rough categorisation of reasoner calculi

Optimal Fixed-Premise Repairs of EL TBoxes: Extended Version

Reasoners can be used to derive implicit consequences from an ontology. Sometimes unwanted consequences are revealed, indicating errors or privacy-sensitive information, and the ontology needs to be appropriately repaired. The classical approach is to remove just enough axioms such that the unwanted consequences vanish. However, this is often too rough since mere axiom deletion also erases many other consequences that might actually be desired. The goal should not be to remove a minimal number of axioms but to modify the ontology such that only a minimal number of consequences is removed, including the unwanted ones. Specifically, a repair should rather be logically entailed by the input ontology, instead of being a subset. To this end, we introduce a framework for computing fixed-premise repairs of $\mathcal{EL}$ TBoxes. In the first variant the conclusions must be generalizations of those in the input TBox, while in the second variant no such restriction is imposed. In both variants, every repair is entailed by an optimal one and, up to equivalence, the set of all optimal repairs can be computed in exponential time. A prototypical implementation is provided. In addition, we show new complexity results regarding gentle repairs.This is an extended version of an article accepted at the 45th German Conference on Artificial Intelligence (KI 2022)

Evidence-based lean logic profiles for conceptual data modelling languages

Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable ALNI). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models

Ontology Evolution Using Ontology Templates

Evolving ontologies by domain experts is difficult and typically cannot be performed without the assistance of an ontology engineer. This process takes long time and often recurrent modeling errors have to be resolved. This paper proposes a technique for creating controlled ontology evolution scenarios that ensure consistency of the possible ontology evolution and give guarrantees to the domain expert that his/her updates do not cause inconsistency. We introduce ontology templates that formalize the notion of controlled evolution and define ontology template consistency checking service together with a consistency checking algorithm. We prove correctness and demonstate the practical use of the techniques in two scenarios

Privacy-Preserving Ontology Publishing:: The Case of Quantified ABoxes w.r.t. a Static Cycle-Restricted EL TBox: Extended Version

We review our recent work on how to compute optimal repairs, optimal compliant anonymizations, and optimal safe anonymizations of ABoxes containing possibly anonymized individuals. The results can be used both to remove erroneous consequences from a knowledge base and to hide secret information before publication of the knowledge base, while keeping as much as possible of the original information.Updated on August 27, 2021. This is an extended version of an article accepted at DL 2021