Third-Party-Funded Research Projects

SFB 884: Political Economy of Reforms

Our society faces great economic and social challenges. There is widespread consensus that reforms are necessary to cope with these challenges. Yet, we experience that reform initiatives are frequently delayed, compromised, or fail altogether. The multidisciplinary SFB 884 aims to provide scientific insights into success and failure of political reforms, determined by competing interests (Project Group A), contexts (Project Group B) and the political process of reform-making (Project Group C). As the core infrastructure, a data center will collect new data on these three dimensions. The DWS Group participates in the second phase of the SFB actively contributing to Project C4 “Measuring a common space and the dynamics of reform positions” and Z1 “Political Text Analysis Network”.

Duration: 2014 - 2021
Overall Budget: 10M EUR overall (500.000 EUR for our group)
Funded by: German Science Foundation (DFG)
Partners: School of Social Sciences and Department of Economics

JOIN-T (Joining Ontologies and semantics INduced from Text)

In this JOIN-T project with the Language Technology Group of TU Darmstadt we aim at delivering new kinds of knowledge bases combining ontological information from large-scale knowledge bases (like Freebase, YAGO or DBpedia) with distributional semantic information encoded within large (i.e. web-scale) amounts of text.

Project duration: 2015 - 2018 (Phase 1) 2019 - 2021 (Phase 2)
Overall budget: 1M EUR (budget Mannheim: ~500.000 EUR)
Funded by: Deutsche Forschungsgemeinschaft (DFG)

Knowledge Consolidation and Organization for Query-specific Wikipedia Construction

The goal of the research project is to make information on the Web accessible in a Wikipedia-like form through a query-driven interaction paradigm. This research requires a combination of methods from information retrieval and automatic text understanding to provide the user with a synthesis of the information through summarization, sub-topic identification, and article organization.

Project duration: 2016 - 2019
Budget: 110.000 EUR
Funded by: Elite program for Post-Docs of the BW-Stiftung 

Data Search for Data Mining (DS4DM) 

As more and more data is available on Web or in the corporate intranets, it is getting increasingly difficult for analysts to find all relevant data for a mining project. The Data Search for Data Mining (DS4DM) project will help anlaysts to find and integrate relevant data by extending the Rapdiminer data mining plattform with data search and data integration features.

Project duration: 2015 - 2018
Overall budget: 400.000 EUR (budget Mannheim: 200.000 EUR)
Funded by: Federal Ministry of Education and Research (BMBF)
Industry partner: Rapidminer GmbH

Identifying Semantic Relations between Models at Different Levels of Granularity

Matching dynamic and static models (business processes or ontologies) has been a vivid research topic over the last decade. Most approaches assume that the models that need to be matched share a similar level of granularity. However, this assumption is often not valid and thus is is required to match, for example, a chain of several activities from a source model to one activity in the target model. Within this project we will develop and evaluate methods that are in particular applicable to such scenarios.

Project duration: 2014 - 2016
Overall budget: 270.000 EUR
Funded by: Deutsche Forschungsgemeinschaft (DFG)

 SyKo²W² (Synthesis of Completion and Correction for Web Knowledge Graphs)

The project will examine how completion and correction methods for knowledge graphs (both of which have been extensively studied in isolation) can be combined into efficient joint methods. For more details please refer to the project page.

Project duration: 2015 - 2017
Overall budget: 150.000 EUR
Funded by: 
MWK Baden Württemberg

InFoLiS 2 - Integration of research data and literature in the Social Sciences

The InFoLiS follow-up project focuses on the identification of references to research data in publications in different languages and domains. To generate and provide the references, a sustainable LOD-infrastructure will be built.

Duration: 2014–2016
Funded by:  Deutsche Forschungsgemeinschaft (DFG)
Project partners: Leibniz Institute for Social Science (GESIS), Mannheim University Library

Mine@LOD - Data Mining with Linked Open Data (DFG)

Many data mining problems can be solved better if more background knowledge is available. The Mine@LOD project aims at developing means to add background knowledge from Linked Open Data to a given data mining problem in a fully automated fashion. 

Project duration: 2013 - 2016
Overall budget: 264.000 EUR
Funded by: Deutsche Forschungsgemeinschaft (DFG)

Demand Forecasting in the Retail Industry

The project is concerned with data mining on time series data of fast moving customer goods (e.g. vegetables, baked goods). In particular, we focus on demand forecasting at different aggregation levels (e.g. intra-day, daily). Therefore, we investigate methods that also enable the integration of external factors that influence the demand (e.g. weather, public holidays).

Duration: 2015 - 2016
Funded by: BMWE EXIST - Existenzgründungen aus der Wissenschaft
Project partner:
OPAL - Operational Analytics GmbH

A New Part-time Master Program in Data Science

In this project, we prepare a new part-time study program, the Master of Data Science (M.Sc. Data Science). This study program will be offered by the HS Albstadt-Sigmaringen in collaboration with the Data and Web Science Group at the Uni Mannheim und the University of Tübingen.

The new Master program addresses the growing need for Data Scientist in industry. Offered as part-time program, it is thus well-suited for students that already have some years of practical industry experience and want to go back to university-level education, while continuing to work in their jobs.

The program starts with the 2015 fall semester (WiSe 2015/16). Further information are available at

Duration: 2014 - 2016

Overall budget: 600.000 EUR
Funded by: MWK Baden Württemberg
Project partner: HS Albstadt-Sigmaringen and University of Tübingen, Germany


Distributed Inference in Probabilistic Knowledge Base

Most of the current information extraction systems, therefore, provide a degree of con dence associated with each extracted statement. The higher this numerical value the more likely is it a-priori that the statement is indeed correct. While probabilistic knowledge bases provide a natural representational framework for this type of problem, probabilistic inference poses a computationally challenging problem. As part of the proposed project, we want to distribute a sampling-based inference algorithm whose input is (a) a large set of statements with confidence values and (b) existing background knowledge, and whose output is a set of statements with a-posteriori probabilities. We propose the development and implementation of a distributed inference algorithm that has two separate processes running on the Hadoop platform. The fi rst process constructs hypergraphs modeling the statements and their conflicts given known background knowledge. The second process runs Markov chains that sample consistent sets of statements from the conflict hypergraph ultimately computing the a-posteriori probabilities of all extracted statements.

Duration: 2012 - 2015
Overall budget: 60.000$
Funded by: Google Faculty Research Award
Project partner:
University of Washington, Seattle