Semistructured Data and Recommendations
Knowledge is structured – until it is stored to a wiki-like information system. The multi-user system SnoopyDB preserves the structure of knowledge without restricting the type or schema of inserted information. A self-learning schema system and recommendation engine support the user during the process of inserting information. These dynamically calculated recommendations develop an implicit schema, which is used by the majority of stored information. Further recommendation measures enhance the content both semantically and syntactically and motivate the user to insert more information than he intended to. Please find screenshots of the SnoopyDB prototype below.
The annotation of online resources enables users to tag photos, bookmarks or bibliographic entries in order to categorize these resources either for personal use or as a collaborative effort for public use. However, due to the fact that tags are simply freely chosen keywords, they often lack context and structure. The SnoopyTagging approach is an online concept and platform which aims at tagging online resources with so-called Structured Tags. These special tags provide users the possibility to additionally specify a context for each tag. Furthermore, a self-learning recommendation engine supports the user and aims at maintaining a homogeneous set of contexts and tags. We showcase this concept by a prototype supporting users in adding Structured Tags to their images on Flickr. Please find
screenshots of the SnoopyDB prototype below.