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AI‐based knowledge generation and representation


Knowledge aware
Knowledge Graphs
for AI
Scalable Training
Data Acquisition


Knowledge aware computing (Prof. Krötzsch)

  • Goal: Enable a tighter integration of knowledge-driven and statistical approaches to AI by laying the foundations for a knowledge-aware computing architecture
    • Design an initial rule-based language for expressive, recursive view definitions over knowledge graphs
    • Investigate the conceptual foundations of integrating other AI methods in this rule-based framework based on well-defined interfaces
    • Develop methods for interpreting and validating results of other AI formalisms with respect to the original knowledge graph.
    • Create a prototype implementation for these concepts, and evaluate it for performance and utility


Knowledge Graphs for AI (Dr. Hellmann, Prof. Rahm)

  • AI-based maintenance and generation of the largest free and open knowledge graph (LOD)
  • Export of millions of derived knowledge graphs for individual AI use cases



Conversational AI: Combining Deep Learning and Large-Scale Knowledge Graphs (Prof. Lehmann)

  • Hybrid AI approach for integrating your data as background knowledge into a dialogue system
  • Investigation of end-to-end learning approaches trained on raw dialogue data.
  • Creation of approaches to help lay users to control expert systems (e.g. robots, data science tool chains)
  • Verbalization strategies to ensure a natural formulation of answers




Web Mining and Crowdsourcing for Scalable Training Data Acquisition (Jun.-Prof. Potthast)

This project studies the aquistion of training data from the web for distant supervision.

  • The Web as Corpus
    • Almost all aspects of society are represented on the web. Many prediction tasks relate to supporting humans.
    • The web is harnessed to fuel machine learning models.
  • Distant supervision
    • For a given task, one does not necessarily need data that ideally matches the problem.
    • Rather, one can get by with loosely matching data.
    • Key goal is the semi-automatic search for such data.
  • Crowdsourcing
    • Many a task requires the additional labeling of data.
    • Scale can only be attained by outsourcing to the crowd.
    • Key goal is the facilitation of crowdsourcing by dedicated tools, and the automation of designing worker interfaces.