Automatic tag recommendation for the web 2.0 blogosphere using collaborative tagging and hybrid ANN semantic structures

This paper proposes a novel approach to automatic tag recommendation for weblogs/blogs. It makes use of collective intelligence extracted from Web 2.0 collaborative tagging as well as word semantics to learn how to predict the best set of tags to use, using a hybrid artificial neural network (ANN). The use of "tags" has recently become very popular as a mean of annotating and organizing everything on the web, from photos, videos and music to blogs. Unfortunately, tagging is a manual process and limited to the users' own knowledge and experience. There may be more accurate or popular tags to describe the same content. Collaborative tagging is a recent technology that creates collective intelligence by observing how different users tag similar content. Our research makes use of this collective intelligence to automatically generate tag suggestions to blog authors based on the semantic content of blog entries.