Defeasible reasoning in Description Logics: an overview on DL^N

DL^N is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on DL^N, illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve DL^N from some recurrent semantic and computational drawbacks. We also compare DL^N with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and DL^N.

Defeasible reasoning in Description Logics: an overview on $\mathcal{DL}^\mathrm{N}$

$\mathcal{DL}^\mathrm{N}$ is a recent approach that extends description logics with defeasible reasoning capabilities. In this paper we provide an overview on $\mathcal{DL}^\mathrm{N}$, illustrating the underlying knowledge engineering requirements as well as the characteristic features that preserve $\mathcal{DL}^\mathrm{N}$ from some recurrent semantic and computational drawbacks. We also compare $\mathcal{DL}^\mathrm{N}$ with some alternative nonmonotonic semantics, enlightening the relationships between the KLM postulates and $\mathcal{DL}^\mathrm{N}$.

Machine Understandable Policies and GDPR Compliance Checking

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.