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