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.
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.
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.