Towards Open Data Mashups for Data Journalism

Fajar J. Ekaputra, Ba Lam Do, Elmar Kiesling, Niina Maarit Novak, Dat Trinh Tuan, A Min Tjoa, Peb Ruswono Aryan: Towards Open Data Mashups for Data Journalism.
In: J. Fern√°ndez, S. Hellmann (eds.): Proceedings of the Posters and Demos Track of the 13th International Conference on Semantic Systems - SEMANTiCS2017,
Amsterdam, The Netherlands, September 11-14, 2017,
online: http://ceur-ws.org/Vol-2044/paper17

In line with a broad social and political movement in recent years, public and private sector actors have started to open up their data and to publish it on various portals. However, the actual use of the available open data sets is still rather limited in many sectors. Consequently, the potentials of available open data sets are not fully realized, which may discourage open data publishers. To encourage a broader adoption of open data and to contribute towards making democratic processes more transparent, critical data journalism is essential. In this demo paper, we propose an approach called Open Data Mashups for Data Journalism (ODMOJO) that aims to bridge open data publishers and their consumers, i.e., journalists and society at large, with Linked Data technologies. Specifically, our approach will facilitate the access, reuse, and integration of open data for General Data Journalism. We plan to evaluate our approach with potential journalism partners as well as open data publishers in Austria, to foster further adoption and utilization of open data through data journalism and Linked Data technologies.


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