The Competence Center "Information Retrieval & Machine Learning" (CC IRML) is working on the semantic collection, intelligent processing and extensive analysis of data and information.

Information Extraction

The "Information Extraction"-Cluster works on the development of methods and tools which support information and data services. These methods and tools comprise of the collection of data from different sources, their enhancement with typed metadata, and the identification of relationships between items. The extracted contents can be validated and relationships between the different contents can be identified. More Information

Smart Information Aggregation

The "Intelligent Information Aggregation"-Cluster investigates methods and tools for modeling the content of natural language texts, focusing on semantic analyses of document collections. We utilize the resulting cross-document structures to create aggregated representations of the information contained in the collection. As a result, we can support the user in her information need by not only presenting a list of relevant documents, which the user must manually scan for the needed information, but by instead providing a much more focused text-form answer or a set of relevant text passages.

Information Filtering

The "Information Retrieval"-Cluster addresses many important issues in the areas of Information Retrieval and Artificial Intelligence, where the most important ones are dealing with the efficient usage of the semantic information that is encapsulated into built indices, the optimization of large search spaces to allow the application of filtering algorithms, and the reduction of the response time in order to allow complex filtering chains with sufficient performance, and more. More Information

Recommender Systems

The "Recommender Systems"-Cluster is focused on finding the correct items for the people at the correct time. Recommender systems use any available information to predict what is and will be interesting for the users. Often, information such as what other people have seen and liked (usage data), or the item content, and user profiles is used to generate recommendations. More Information

User Modeling and Adaptivity

The "User Modeling and Adaptivity"-Cluster works on advanced methods and tools that allow collecting, aggregating and understanding user data and user behavior. This user information will then be processed and enriched with semantic information. This enriched information serves as a basis for personalization of adaptive systems and recommender systems as well as for the Smart Layouting of news articles. More Information