Knowledge Relationship Discovery

The core topic of our research activities is the transformation of distributed, heterogeneous information into knowledge which is relevant for users. One of the major challenges in this regard is the constantly growing number of massive, complex and dynamic information sources. Efficient search and reliable retrieval of relevant information has become a decisive productivity and innovation factor in this context. Advanced discovery methods are capable of automatically identifying relevant knowledge in information sources and delivering such knowledge directly to a users workplace based on current work context. Knowledge relationship discovery therefore provides the fundament for efficient and successful knowledge work.

Our vision is to integrate content-based and semantic discovery methods, in order to bridge the semantic gap between information and meaning in knowledge spaces.

Research & Development Focus

• Semantic enrichment of unstructured information sources
• Semantic integration of heterogeneous information sources
• Visually supported retrieval and analysis methods for large, heterogeneous information sources

Additional Competencies

• Detecting semantic entities in unstructured information sources
• Identifying semantic relations in and between information sources
• Cross-language and cross-domain search and retrieval
• Automatic analysis of information source structure and qualtiy
• User interfaces for visual analysis of large information repositories
• User interfaces for visual navigation of semantic structures