Using Web Annotations to Represent Relations between Structured and Unstructured Information in Semantic Wikis

Hybri Wikis combine unstructured information in the form of Wiki texts with structured information. However, there is no link between the structured and the unstructured information. Therefore, if a piece of information is represented as both, structured and unstructured information, it has to be update manually in both formats in order to keep the content consistent.

Aim of this thesis is to use the metamodel of SocioCortex and Machine Learning to automatically link structured and unstructured data in Hybrid Wikis and also extract structured information from unstructured content. A web application should be built in order to allow users to manually annotate unstructured data from SocioCortex in order to create training data for the Machine Learning algorithm and visualize the links between the data format.