Distributed Identity Management

Semantics is a local and a global problem at the same time. Local because is in the mind of the people who have personal interpretations, and global because we need to reach a common understanding by sharing and aligning these personal interpretations. As opposed to current state-of-the-art approaches based on a two layer architecture (local and global), we deal with this problem by designing a general three layer architecture rooted on the personal, social, and universal levels. The new intermediate social level acts as a global level for the personal level, where semantics is managed around communities focusing on specific domains, and as local for the universal level as it only deals with one part of universal knowledge. For any of these layers there are three main components of knowledge that helps us encode the semantics at the right granularity. These are: i) Concrete knowledge, which allows us to achieve semantic compatibility at the level of entities, the things we want to talk about; ii) Schematic knowledge, which defines the structure and methods of the entities; and iii) Background knowledge, which enables compatibility at the language level used to describe and structure entities. The contribution of this work is threefold: i) the definition of general architecture for managing semantics of entities, ii) the development components of the system based on the architecture; these are structure preserving semantic matching and sense induction algorithms, and iii) the evaluation of these components with the creation of new gold standards datasets.