Wikipaintings Dataset

The wikipaintings collection is a subset of paintings manually labelled according to the art epoch of the respective art epoch. All paintings and labels have been crawled from

WikiArt presents both public domain and copyright protected artworks. The latter are showcased in accordance with fair use principle:

refer - Semantic Annotation and Information Visualization

refer is an annotation, visualization and recommendation system based on Linked Open Data and Semantic Web Technologies. It aims to improve the user’s and author’s experience while curating and navigating in blogs, multimedia platforms, and archives and is implemented as a freely available Wordpress plugin. For more information, please visit

Semantic Games

Linked Data is a perfect source to generate quiz games for arbitrary purposes. Games provide an incentive for many people to test or to challenge their knowledge. While playing the games all players can contribute to various tasks including ground-truth generation, data cleansing, or simply assessment. We aim to harness games-with-a-purpose (GWAP) approaches to create and curate semantic content.

Evaluation on Semantic Search

We need your help with a research project on semantic search algorithms!

We have developed new approaches, which of course have to be evaluated - and this is where we need you! Please assess the quality of our algorithms by telling us which documents are relevant to a search query, and by comparing different rankings. The evaluation can be done anywhere, anytime in the next two weeks, at You can also stop or pause it whenever you want.

3rd International Workshop on Semantic Web Enterprise Adoption and Best Practice (WaSABi2015)

The WaSABi workshop aims at helping to guide the conversation between the scientific research community, IT practitioners and industry. The workshop helps to establish best practices for the development, deployment and evaluation of Semantic Web based technologies. Both research and industry communities can benefit greatly from this discussion by sharing use cases, user stories, practical development issues, evalutaions and design patterns.

KEA - Named Entity Recognition, Disambiguation, Linking

KEA is a named entity annotation system based on a fine-granular context model taking into account heterogeneous text sources as well as text created by automated multimedia analysis. The source texts can have different levels of accuracy, completeness, granularity and reliability which influence the determination of the current context. Ambiguity is solved by selecting entity candidates with the highest level of probability according to the predetermined context.


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