We present a new architecture for decentralized user modeling and briefly discuss the user model markup language USERML, the general user model ontology GUMO for the uniform interpretation of decentralized user models, and the integration of ubiquitous applications with the u2m.org user model service. The motivation is that ubiquitous evaluation of user behavior with a variety of systems in the web or the physical world might lead to attractive new services. 1 Approach and Architecture We developed the RDF-based user model exchange language UserML to enable decentralized systems to communicate over user models. The idea is to spread the information among all adaptive systems, either with a mobile device or via ubiquitous networks. UserML statements can be arranged and stored in distributed repositories in XML, RDF or SQL. Each mobile and stationary device has an own repository of situational statements, either local or global, dependent on the network accessability. A mobile device can perfectly be integrated via wireless lan or bluetooth into the intelligent environment, while a stationary device could be isolated without network access. The different applications or agents produce or use UserML statements Fig. 1. The syntax-semantics interplay between USERML and GUMO to represent the user model information. UserML forms the syntactic description in the knowledge exchange process, see figure 1. Each concept like the user model auxiliary 62 Dominik Heckmann et al. hasProperty and the user model dimension timePressure points to a semantical definition of this concept which is either defined in the general user model ontology GUMO, the UbisWorld ontology, which is specialized for ubiquitous computing, or the general SUMO/MILO ontology, see [1]. The merging of partial, decentralized user models is realized by combining the different user model repositories, while the inferential integration is done by filters and conflict resolution strategies as shown in figure 2(b). Figure 2(a) and figure 2(c) show the upward and downward inference from repositories or journals to the user model and vice versa.
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