Reasoning in Description Logic Ontologies for Privacy Management

Abstract

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.

Using Rough Set Theory for Reasoning on Vague Ontologies

“…We say that the interpretation I is a possible world (model) of an ontology ,  denoted by I  if I satisfies all the axioms in and all the assertions in . The reasoning task is to check concept and role instances and to answer queries over a satisfied ontology [29,30]. So, the first step is to verify the ontology satisfaction (verify if ontology admits at least one model).…”

Ontologies and Data Management: A Brief Survey

“…-Learning (ontologies and queries): see the survey [313] -Privacy management: see [102,306,367 [10, 12, 164, 360, 361] -Relaxations of query answering semantics: see [146,147] -Ranking OMQ answers: see [368]…”