Semantic Modeling for Automated Decisions
Share this Session:
  Klaus Reichenberger   Klaus Reichenberger
managing director
intelligent views


Tuesday, August 18, 2015
02:00 PM - 02:45 PM

Level:  Case Study

Semantic models provide a good data basis for intelligent systems from decision automation to data analysis. They complement black-box statistical approaches with an analytic body of knowledge – this way they can add transparency and intervention options to machine learning, optimization or text analysis methods.

This session will discuss semantic modeling practices for automated decisions from a variety of industry projects:

  • How do we integrate learning's from statistical analyses in semantic model and, conversely, use the model as background for analysis?
  • How much do we have to model and how do we know when to stop?
  • How do we deal with uncertain and incomplete knowledge?
  • How do we engage the domain experts in the knowledge engineering?
Project examples include:
  • Compatibility of products based on their characteristics
  • Job and Business Matching
  • Prediction of the success probability of legal claims
  • Making sense of sensor data in traffic analysis
  • Resource optimization constrained by complex business rules

Over the last years, Klaus Reichenberger developed together with his team solutions for more than 40 clients - based on intelligent views' semantic graph database k-infinity. He has over 15 years of experience in the field of semantic technology and graph-based representation of knowledge. He is author of numerous publications and a frequent speaker on semantic modelling and its applications in the enterprise. Prior to founding intelligent views, Klaus Reichenberger worked as a researcher for the Fraunhofer-Gesellschaft bringing together knowledge representation and visualization techniques.

Close Window