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CENTER FOR
SCALABLE DATA ANALYTICS
AND ARTIFICIAL INTELLIGENCE

AI‐based knowledge generation and representation

 

Knowledge aware
computing
Knowledge Graphs
for AI
Conversational
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.