Try it @ http://apps.facebook.com/movie-recommendation/

This is a Facebook application for movie recommendation. The content-based recommender engine behind MORE is fed exclusively with Linked Open Data. All the information related to the domain of movie is retrieved from RDF datasets and used to recommend movies to the end user based on the items they previously selected. The knowledge available in the datasets is also exploited to provide explanations on the reason why a movie has been suggested.

Try it @ http://sisinflab.poliba.it/not-only-tags/

NOT is a proof-of-concept application for the creation of semantic-based annotation tools. By exploiting the information encoded in DBpedia, NOT is able to suggest related tags to the user and, once selected, to semantically expand them. NOT can be embedded in third-parties applications. To see NOT in action, take a look at I.M.P.A.K.T.-revolution

Try it @ http://sisinflab.poliba.it/led/

LED leverages information available in DBpedia to suggest new terms related to a user query for query refinement and exploratory browsing tasks. Via an intuitive interface, the user is allowed to explore the knowledge domain related to a given query and to refine this latter by using the terms suggested by the system.

Try it @ http://sisinflab.poliba.it/impakt-revolution/

IMPAKT-revolution is Web tool to create an RDFa-enabled version of a Curriculum Vitae. It exploits data coming from DBpedia to allow users to semantically annotate sections of their CV via RDFa.