Noname manuscript No. (will be inserted by the editor) Automatic News Recommendations via Aggregated Profiling Erik Mannens · Sam Coppens · Toon De Pessemier · Hendrik Dacquin · Davy Van Deursen · Robbie De Sutter · Rik Van de Walle Received: date / Accepted: date E. Mannens Ghent University - IBBT ELIS - Multimedia Lab Ghent, Belgium E-mail:
[email protected] S. Coppens Ghent University - IBBT ELIS - Multimedia Lab Ghent, Belgium E-mail:
[email protected] T. De Pessemier Ghent University - IBBT INTEC - WiCa Ghent, Belgium E-mail:
[email protected] H. Dacquin VRT VRT-medialab Brussels, Belgium E-mail:
[email protected] D. Van Deursen E-mail:
[email protected] Ghent University - IBBT ELIS - Multimedia Lab Ghent, Belgium E-mail:
[email protected] R. De Sutter VRT VRT-medialab Brussels, Belgium E-mail:
[email protected] R. Van de Walle E-mail:
[email protected] Ghent University - IBBT ELIS - Multimedia Lab Ghent, Belgium E-mail:
[email protected] 2 Abstract Today, people have only limited, valuable leisure time at their hands which they want to fill in as good as possible according to their own interests, whereas broad- casters want to produce and distribute news items as fast and targeted as possible. These (developing) news stories can be characterised as dynamic, chained, and dis- tributed events in addition to which it is important to aggregate, link, enrich, recom- mend, and distribute these news event items as targeted as possible to the individual, interested user. In this paper, we show how personalised recommendation and distribu- tion of news events, described using an RDF/OWL representation of the NewsML-G2 standard, can be enabled by automatically categorising and enriching news events metadata via smart indexing and linked open datasets available on the web of data.