refer: a Linked Data based Text Annotation and Recommender System for Wordpress

When searching for an arbitrary subject in weblogs or archives, users often don’t get the information they are really looking for, because they are overwhelmed with an overflow of information while sometimes the presented information is too scarce to make any use of it. Without further knowledge about the context or background of the intended subject users are easily frustrated because they either cannot handle the amount of information or they might give up because they cannot make sense of the topic at all. Furthermore, authors of online-platforms often deal with the issue to provide useful recommendations of other articles and to motivate the readers to explore more of the available but often hidden content of their blog or archive. In the demo presentation, we present refer, a semantic annotation and visualization system integrated into the Wordpress platform. refer enables content creators to (semi-)automatically annotate their texts with DBpedia resources as part of the original writing process and visualize them automatically. With refer users are encouraged to take an active part in discovering a platform’s information content interactively. They can discover background information as well as relationships among persons, places, events, and anything related to the subject in current focus and are inspired to navigate the previously hidden information on a platform. Related systems include Pundit [2] and WYSIWYM [1]. The original concept of the presented user interface and a first prototype have already been presented in [4].