An approach for semantic enrichment of social media resources for context dependent processing

This diploma thesis provides the functional basis for information filtering in the domain of complexity. It helps to create the domain-specific, adaptive portal CompleXys, that filters blog entries and similar social media resources according to their relevance to a specific context.

The first of two required modules, that are developed throughout this work, is a semantic enrichment module. Its purpose is to extract and provide semantic data for each input document. This semantic data should be appropriate for a relevance decision to the domain of complexity as well as for further usage in the filter module. It utilizes various approaches to perform a multi-label text classification onto a fixed complexity thesaurus.

The second implemented module is a content filter module. It provides a dynamic system of filters, which forms an access interface to the document store. It uses the previously extracted annotation and classification data to enable complex, semantically based filter queries.

Though the total system performance will only be testable after the complete system is implemented, this thesis also conducts a first proof-of-concept evaluation of the two created modules. It investigates the classification quality of the semantic enrichment module as well as the response time behavior of the content filter module.