Information Service Engineering
     Information Service Engineering (ISE) focuses on researching, developing, and refining symbolic knowledge representations, sub-symbolic AI technologies, and their hybrid integration. Our goal is to advance state-of-the-art methodologies by exploring their applications  in real-world contexts. A core focus is the interplay between symbolic and sub-symbolic AI, investigating how knowledge graphs and ontologies can enhance deep learning and language models, and vice versa. ISE conducts applied research in semantic indexing, aggregation, linking, and retrieval for comprehensive, heterogeneous, and distributed data sources. Solutions for knowledge extraction, semantic annotation, semantic and exploratory search, recommender systems, and question answering are developed within this  context. Beyond methodological research, ISE engages in applied projects across cultural heritage, digital humanities, materials science, and research data management.
ISE is structured in two departments: Knowledge Graphs (ISE-KG) and Machine Learning (ISE-ML).
ISE is structured in two departments: Knowledge Graphs (ISE-KG) and Machine Learning (ISE-ML).
 
  Contact
  Prof. Dr. Harald Sack
Vice President Information Service Engineering
      
      
          harald.sack [at] fiz-karlsruhe.de
      
      
      
      Knowledge Graphs (ISE-KG) 
      
        Machine Learning (ISE-ML) 
      
        FIZ ISE @ Social Media 
      
        Completed Projects 
      
        News 
      
        Dissertations 
      
        Master Theses  
      
        Bachelor Theses 
      
        Awards and Prizes 
      
        Teaching 
      
        Inaugural lecture as video 
      
        Publications 
      
       
  Contact
  Prof. Dr. Harald Sack
Vice President Information Service Engineering
      
      
          harald.sack [at] fiz-karlsruhe.de
      
      
      
     
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
  