Artificial Intelligence Group (Prof. Stuckenschmidt)

Our group conducts fundamental and applied research in knowledge representation formalisms with a focus on reasoning techniques for information extraction and integration. Our work is centered around:

  • Standard reasoning including description logic reasoning and non-standard reasoning techniques (explanations and debugging techniques)
  • Combining Probabilistic reasoning and Utility Theory with Description Logics
  • Applications in various domains (web data integration and management, semantic web, ontology matching, context-aware applications, Business Process Management etc.)  

People

Faculty

Postdoctoral Researchers

Researchers

PhD Students

  • Taha Alhersh: Ego-centric Vision
  • Sarah Alturki: Educational Data Mining
  • Fabian Burzlaff: (InES) Data Integration in Industry 4.0
  • Oliver Frendo (SAP) Decision Support in Electro-Mobility
  • David Friede: Mathematical Optimization for AI Applications
  • Elena Kuss: Process Matching
  • Andreas Nolle (HS Albstadt-Sigmaringen): Ontology-based Data Access, Debugging
  • Bernhard Schäfer (SAP): Intelligent Process Automation
  • Christian Schreckenberber: (InES) Data Mining in dynamic environments
  • Chrstoph Theil: Automatic Content Analysis for Finance and Accounting Research

Alumni (only finished PhDs)

* With Distinction

Selected Publications

Markov Logic Reasoning:

  • Melisachew Wudage Chekol, Giuseppe Pirrò, Jörg Schönfisch and Heiner Stuckenschmidt Marrying uncertainty and time in knowledge graphs. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence and the Twenty-Ninth Innovative Applications of Artificial Intelligence Conference : February 4–9, 2017, San Francisco, California USA; 88-94. AAAI Press, San Francisco, CA, 2017.
  • Melisachew Wudage Chekol, Jakob Huber, Christian Meilicke, Heiner Stuckenschmidt : Markov Logic Networks with Numerical Constraints. In: Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, Netherlands.
  • Jan Noessner, Mathias Niepert and Heiner Stuckenschmidt. RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2012, Bellevue, Washington, USA; 739-745. AAAI Press, Menlo Park, Calif., 2013

Activity Recognition:

Semantic Matching:

Projects

Projects at the chair

  • DFG Project: Explain - Knowledge-based Analysis of Argumentation in a formal Argumentation Inference System (Part of SPP RATIO - Robust Argumentation Machines
  • DFG Project: Practical Probabilistic Reasoning in Knowledge Graphs
  • SFB 884: Political Economy of Reforms

 

Projects at the InES Institute

  • Learning Data Transformation Rules for Data Migration (SNP SE)

 

Teaching and Infrastructure Projects

  • Part-time Master Data Science in cooperation with Hochchule Albstadt-Sigmaringen

Software and Data

Courses

Publications

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