Prof. Dr. Achim Rettinger

• Achim Rettinger is a full professor at Trier University for Natural Language Processing, where he is leading the research group on Knowledge Representation Learning (krAil). He is head of the Department for Computational Linguistics and Digital Humanities at Trier University. He also leads a group on applied research  at the FZI Forschungszentrum Informatik where he is a director. His academic background is in computer science and artificial intelligence with a focus on machine learning and knowledge representation. Before joining Trier University, he studied and worked at the Artificial Intelligence Center at University of Georgia (USA), the Alberta Machine Intelligence Institute at University of Alberta (Canada), the Technische Universität München, the Siemens AG and at Karlsruhe Institute of Technology.

Contact

• Prof. Dr. Achim Rettinger Universität Trier Faculty II: English Studies, Computational Linguistics & Digital Humanities, Literature, Japanese Studies, Classical Philology, Media Studies, Phonetics, Romance Studies, Sinology, Slavic Studies Knowledge Representation Learning Building B, Room 212 - 216 Universitätsring 15 54296 Trier 0651-201 2271, 0651-201 3946 rettinger@uni-trier.de
  Prof. Dr. Achim Rettinger

Basic Research

• Prof. Rettinger's research is concerned with the machine learning of expressive knowledge representations, specifically from natural language in digital media (text and image) and from knowledge bases. The goal is to build representation that are accessible to both, humans and computers while allowing expressive and explainable analysis and inference. To achieve that he develops methods that combine Machine Learning, specifically Deep Learning and Language Models, with Symbolic Knowledge Representation, specifically Knowledge Graphs and Semantic Technologies. Prof. Rettinger publishes in a wide range of research areas, like machine learning, natural language understanding and knowledge representation.

Special Expertise

• Basic Research

  • Machine Learning (ML): Representation Learning, Self-Supervised Learning, Artificial Neural Network (ANN), Generative Models
  • Technology Analysis: Human-AI Interaction



• Application related Research

  • Language and Text Comprehension: Natural Language Processing (NLP), Natural Language Generation
  • Information Retrieval (Knowledge / Data Management and Analysis): Knowledge Discovery in Databases (Data Mining)
  • Technology Analysis: Validation