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 http://refer.cx.
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.
The MIRFLICKR-1M-s16a dataset provides an extension to the official MIRFLICKR-1M collection. Additional user generated annotation data has been downloaded and deep feature representations have been extracted for all 1 million images. All data is made publicly available for research purposes.
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 http://s16a.org/percy. You can also stop or pause it whenever you want.
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 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.
Recently, we have been working on the DBpedia / Wikipedia Page Link dataset. We have considered the English and the German language versions for this project. In the current DBpedia 2014 page links English and German datasets 19 million and 7 million entities are represented respectively. But the original DBpedia only contains about 4 million and 1 million distinct entities for English and German versions.
Since the start of the Linked Open Data Cloud, we have seen an unprecedented volume of structured data published on the web, in most cases as RDF and Linked (Open) Data. The quality of these datasets can hardly be better than the original data source. We see datasets originating from crowdsourced sources like Wikipedia and OpenStreetMap and highly curated sources e.g. from the library domain. Quality is of course fitness for use, thus DBpedia currently can be appropriate for a simple end-user application but could never be used in the medical domain for treatment decisions.