Research

The central challenge of nowadays information society is to take advantage of the deluge of information that is available on the Web as well as in enterprise contexts.

In order to support companies, public institutions, and individuals to facilitate the available information for their needs, the Data and Web ScienceGroup conducts research on methods for managing, integrating, and mining large-amounts of heterogeneous information within enterprise and open Web contexts.

Our research is focused on the following areas:

• Web-based Systems (Prof. Dr. Bizer)
• Data Analytics (Prof. Dr. Gemulla)
• Web Data Mining (Prof. Dr. Paulheim)
• Natural Language Processing (Prof. Dr. Ponzetto and Prof. Dr. Glavaš)
• Artificial Intelligence (Prof. Dr. Stuckenschmidt)
• Process Analytics (Prof. Dr. Van der Aa)

The focus area Web-based Systems explores technical and empirical questions concerning the development of global, decentralized information environments. Our current focus is the evolution of the World Wide Web from a medium for the publication of documents into a global dataspace. Our empirical work is accompanying this evolution by monitoring the adoption of Semantic Markup and Linked Data technologies on the Web. Our technical work focuses on integrating data from large numbers of Web data sources and includes topics such as information extraction, identity resolution, schema matching, data fusion, and data search. We apply the developed methods for the tasks of integrating product data from large numbers of e-shops as well as for creating large-scale knowledge bases such as DBpedia. More information ...

Our group's research focuses on systems and methods for analyzing and mining large datasets as well as their application in practice. Our research interests include data analysis and data mining, text mining and information extraction, optimization, approximation techniques, and algorithms for modern hardware.

The group carries out research on methods for mining knowledge from large amounts of structured and unstructured data on the Web. In order to address the challenges of Web-scale data mining in terms of the size, heterogeneity, and dynamics of the data, a focus is on supervised or unsupervised methods that combine logical and statistical inference. Existing and automatically acquired knowledge is used to facilitate data integration, enrichment, and cleansing, as well as to bootstrap the overall data mining process. More information ...

The NLP and IR group at DWS conducts research on methods for knowledge acquisition and natural language processing (NLP), as well as their application to support empirical research in (Computational) Social Sciences and (Digital) Humanities. In our work, we investigate a wide range of techniques for text understanding – ranging from representation learning and distributional semantics all the way through symbolic, entity-based approaches leveraging wide-coverage knowledge graphs – and apply these to a wide range of research topics such as such computational semantics, multilinguality, information retrieval and multimodal NLP, to name a few. More information ...

The Chair is performing fundamental and applied research in a wide range of topics in Artificial Intelligence including Knowledge Representation, Machine Learning, Natural Language Processing and Decision Theory.  The group has an international reputation for work on information integration, combining logical and probabilistic reasoning and human activity recognition. The chair closely cooperates with the Institute for Enterprise Systems (InES) and has applied AI techniques in a number of projects in areas like Healthcare, Finance, Automotive and Retail. The group has successfully carried out industry funded projects in cooperation with large companies as well as startups and small technology companies and is constantly looking for new challenges and opportunities to show the benefits of AI methods in real world applications. More information ...