Johann Stan

Using linked data to reduce learning latency for e-book readers

Web 2.0 technologies provide an opportunity to transform learning into a social experience: social learning can directly benefit from user-generated content analysis. The e-book device is an object central to the learning process; it becomes an ideal medium to promote innovative learning tools. In this paper, we describe how we analyse user annotations in e-books using Linked Data to reduce the latency between professor knowledge, book content and student assimilation.

Semantic User Interaction Profiles for Better People Recommendation

In this paper we present a methodology for learning user profiles from content shared by people on Social Platforms. Such profiles are specifically tailored to reflect the users degree of interactivity related to the topics they are writing about. The main novelty in our work is the introduction of Linked Data in the content extraction process and the definition of specific scores to measure expertise and interactivity.

Developing Compelling Social-Enabled Applications with Context-Based Social Interaction Analysis

We present in this paper a new approach for constructing implicit social networks from electronic sources, like emails, SMS and phone calls. The main novelty is the use of Social Interaction Analysis to assist end-users in their communication needs. We discuss how a social proximity can be calculated between two persons in a social network and show how a weighted, directed network is constructed based on interactions between people. After a description of the architecture of the framework, we show how a contextual, weighted social network can help in automatically finding the contact with highest probability to know the whereabouts of a person who is currently not reachable. The implemented social helper application uses a specific contextual view of the social network, exploiting only interactions that occurred in the last 48 hours.