The framework developed in this paper can deal with scenarios where selected sub-ontologies of a large ontology are offered as views to users, based on criteria like the user's access right, the trust level required by the application, or the level of detail requested by the user. Instead of materializing a large number of different sub-ontologies, we propose to keep just one ontology, but equip each axiom with a label from an appropriate labeling lattice. The access right, required trust level, etc. is then also represented by a label (called user label) from this lattice, and the corresponding sub-ontology is determined by comparing this label with the axiom labels. For large-scale ontologies, certain consequence (like the concept hierarchy) are often precomputed. Instead of precomputing these consequences for every possible sub-ontology, our approach computes just one label for each consequence such that a comparison of the user label with the consequence label determines whether the consequence follows from the corresponding sub-ontology or not. In this paper we determine under which restrictions on the user and axiom labels such consequence labels (called boundaries) always exist, describe different black-box approaches for computing boundaries, and present first experimental results that compare the efficiency of these approaches on large real-world ontologies. Black-box means that, rather than requiring modifications of existing reasoning procedures, these approaches can use such procedures directly as sub-procedures, which allows us to employ existing highly-optimized reasoners.

The framework developed in this paper can deal with scenarios where selected sub-ontologies of a large ontology are offered as views to users, based on contexts like the access rights of a user, the trust level required by the application, or the level of detail requested by the user. Instead of materializing a large number of different sub-ontologies, we propose to keep just one ontology, but equip each axiom with a label from an appropriate context lattice. The different contexts of this ontology are then also expressed by elements of this lattice. For large-scale ontologies, certain consequences (like the subsumption hierarchy) are often pre-computed. Instead of pre-computing these consequences for every context, our approach computes just one label (called a boundary) for each consequence such that a comparison of the user label with the consequence label determines whether the consequence follows from the sub-ontology determined by the context. We describe different black-box approaches for computing boundaries, and present first experimental results that compare the efficiency of these approaches on large real-world ontologies. Black-box means that, rather than requiring modifications of existing reasoning procedures, these approaches can use such procedures directly as sub-procedures, which allows us to employ existing highly-optimized reasoners. Similar to designing ontologies, the process of assigning axiom labels is error-prone. For this reason, we also address the problem of how to repair the labelling of an ontology in case the knowledge engineer notices that the computed boundary of a consequence does not coincide with her intuition regarding in which context the consequence should or should not be visible.

Abstract. Role Based Access Control (RBAC) is a methodology for providing users in an IT system specific permissions like write or read to users. It abstracts from specific users and binds permissions to user roles. Similarly, one can abstract from specific documents and bind permission to document types. In this paper, we apply Description Logics (DLs) to formalize RBAC. We provide a thorough discussion on different possible interpretations of RBAC matrices and how DLs can be used to capture the RBAC constraints. We show moreover that with DLs, we can express more intended constraints than it can be done in the common RBAC approach, thus proving the benefit of using DLs in the RBAC setting. For deriving additional constraints, we introduce a strict methodology, based on attribute exploration method known from Formal Concept Analysis. The attribute exploration allows to systematically finding unintended implications and to deriving constraints and making them explicit. Finally, we apply our approach to a real-life example.

Abstract. Recent research has shown that annotations are useful for representing access restrictions to the axioms of an ontology and their implicit consequences. Previous work focused on assigning a label, representing its access level, to each consequence from a given ontology. However, a security administrator might not be satisfied with the access level obtained through these methods. In this case, one is interested in finding which axioms would need to get their access restrictions modified in order to get the desired label for the consequence. In this paper we look at this problem and present algorithms for solving it with a variety of optimizations. We also present first experimental results on large scale ontologies, which show that our methods perform well in practice.

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