In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.

In this paper we perform a preliminary evaluation on how Semantic Web technologies such as RDF and OWL can be used to perform textual encoding. Among the potential advantages, we notice how RDF, given its conceptual graph structure, appears naturally suited to deal with overlapping hierarchies of annotations, something notoriously problematic using classic XML based markup. To conclude, we show how complex querying can be performed using slight modifications of already existing Semantic Web query tools.