LinkedUp – Vici Competition Awards

The winners of the Linked Vici Competition, our third and final competition on open and linked data for educational purposes, have been announced at ISWC 2014, the 13th International Semantic Web Conference, in Riva del Garda, Italy.
Our work, the Visualization of Water Resources & Ecology, which provides rich means to search journals, tweets and Wikipedia annotations was awarded with the 3rd prize. The interactive visualizations address the Focused Track ‘Water Resources & Ecology’, proposed and supported by Elsevier, to see how linked data can be used for making the learning experience more appealing and enhanced. The reviewers spent quite some time clicking around and were “overall happy with the interface and with the data”.
Additionally, our work was the winner of the People’s Choice Award, which was selected by the participants of the ISWC 2014 conference.

Authors: Ricardo Kawase, Patrick Siehndel and Ujwal Gadiraju

PDF: kawase-vici

Demo: http://l3s.de/~kawase/vici/

 

Towards a Semantically Enriched Online Newspaper

The Internet plays a major role as a source of news. Many publishers offer online versions of their newspapers to paying customers. Online newspapers bear more similarity with traditional print papers than with regular news sites. In a close collaboration with Mediengruppe Madsack – publisher of newspapers in several German federal states, we aim at providing a semantically enriched online newspaper. News articles are annotated with relevant entities – places, persons and organizations. These annotations form the basis for an entity-based `Theme Radar’, a dashboard for monitoring articles related to the users’ explicitly indicated and inferred interests.

Authors: Ricardo Kawase, Eelco Herder, Patrick Siehndel

PDF: kawase-iswc2014

Extracting architectural patterns from Web data

Knowledge about the reception of architectural structures is crucial for architects or urban planners. Yet obtaining such information has been a challenging and costly activity. With the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information about the building’s features and characteristics (for instance, through public Linked Data). In this paper, we present the first step towards the exploitation of structured data available in the Linked Open Data cloud, in order to determine well-perceived architectural patterns.

Authors: Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze

PDF:gadiraju-iswc2014

Identifying Topic-Related Hyperlinks in the Crowd

The microblogging service Twitter has become one of the most popular sources of real time information. Every second, hundreds of URLs are posted on Twitter.
Due to the maximum tweet length of 140 characters, these URLs are in most cases a shortened version of the original URLs. In contrast to the original URLS, which usually provide some hints on the destination Web site and the specific page, shortened links do not tell the users what to expect behind them. These links might contain relevant information or news regarding a certain topic of interest, but they might just as well be completely irrelevant, or even lead to a malicious or harmful website. In this paper, we present our work towards identifying credible Twitter users for given topics. We achieve this by characterizing the content of the posted URLs to further relate to the expertise of Twitter users.

Authors: Patrick Siehndel, Ricardo Kawase, Eelco Herder, Thomas Risse

PDF: siehndel-iswc2014