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 enterprises, research institutions and individuals to facilitate the available information for their needs, the Data and Web Science Group conducts research on methods for managing, integrating and mining large-amounts of heterogeneous information within enterprise and open Web contexts.

Our research group consists of the following focus groups:

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 research is accompanying this evolution by monitoring the deployment of Linked Data and semantic markup technologies on the Web. Our technical work focuses on integrating data from large numbers of Web data sources and includes topics such as identity resolution, schema matching, data provenance and data fusion. 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 group conducts research on knowledge acquisition from heterogeneous Web sources – ranging from large raw text collections all the way through collaboratively constructed resources (e.g., Wikipedia) – and its application to Natural Language Processing (NLP), Information Analysis and Retrieval. Areas of interest include “deep” NLP techniques for lexical semantics (Word Sense Disambiguation, ontology-based and distributional approaches to semantic similarity), as well as for document understanding and structuring (entity linking, co-reference resolution, discourse coherence, automatic summarization). The group applies NLP methods to support empirical research in Social Science and Humanities. More information ...

The group is carrying out fundamental and applied research on the development and application of AI methods and tools to the problem of web data interpretation and management. The focus is on inductive and deductive reasoning for information extraction and -integration. The work ranges from logical reasoning using description logics and logic programming to statistical learning and inference methods in particular statistical relational learning and log-linear models. The group applies reasoning methods to all kinds of data ranging from structured data to free texts. A special interest of the group is on distributed algorithms for large scale reasoning. More information ...

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 ...