Context as foundation for a semantic desktop

Adoption of semantic web technologies and principles presents an opportunity to change the conceptual model of desktop computing. Moving from a traditional position where the desktop is largely tied to a specific computational device, a semantic desktop could exist as a broad, networked space defined relative to the user. In this position paper we argue that personal, computing, and knowledge contexts are the appropriate means by which to define and shape the desktop space, and that collectively they provide the foundation for novel functionality in a semantic desktop

A spiral model for adding automatic, adaptive authoring to adaptive hypermedia

At present a large amount of research exists into the design and implementation of adaptive systems. However, not many target the complex task of authoring in such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck for authoring of adaptive hypermedia. One such solution is the reuse of automatically generated metadata. In our previous work we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT ( My Online Teacher), and a semantic desktop environment, indexed by Beagle++. A prototype, Sesame2MOT Enricher v1, was built based upon this integration approach and evaluated. After the initial evaluations, a web-based prototype was built (web-based Sesame2MOT Enricher v2 application) and integrated in MOT v2, conforming with the findings of the first set of evaluations. This new prototype underwent another evaluation. This paper thus does a synthesis of the approach in general, the initial prototype, with its first evaluations, the improved prototype and the first results from the most recent evaluation round, following the next implementation cycle of the spiral model [Boehm, 88]

Managed Forgetting to Support Information Management and Knowledge Work

Trends like digital transformation even intensify the already overwhelming mass of information knowledge workers face in their daily life. To counter this, we have been investigating knowledge work and information management support measures inspired by human forgetting. In this paper, we give an overview of solutions we have found during the last five years as well as challenges that still need to be tackled. Additionally, we share experiences gained with the prototype of a first forgetful information system used 24/7 in our daily work for the last three years. We also address the untapped potential of more explicated user context as well as features inspired by Memory Inhibition, which is our current focus of research.Comment: 10 pages, 2 figures, preprint, final version to appear in KI - K\"unstliche Intelligenz, Special Issue: Intentional Forgettin

Understanding personal data as a space - learning from dataspaces to create linked personal data

In this paper we argue that the space of personal data is a dataspace as defined by Franklin et al. We define a personal dataspace, as the space of all personal data belonging to a user, and we describe the logical components of the dataspace. We describe a Personal Dataspace Support Platform (PDSP) as a set of services to provide a unified view over the user’s data, and to enable new and more complex workflows over it. We show the differences from a DSSP to a PDSP, and how the latter can be realized using Web protocols and Linked APIs.<br/

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones