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DESCRIPTION:Click for Latest Location Information: http://smartdata2015.dataversity.net/sessionPop.cfm?confid=91&proposalid=7720\nSemantic 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.\nThis session will discuss semantic modeling practices for automated decisions from a variety of industry projects:\nHow do we integrate learning's from statistical analyses in semantic model and, conversely, use the model as background for analysis?\nHow much do we have to model and how do we know when to stop?\nHow do we deal with uncertain and incomplete knowledge?\nHow do we engage the domain experts in the knowledge engineering?  \nProject examples include:\nCompatibility of products based on their characteristics \nJob and Business Matching \nPrediction of the success probability of legal claims\nMaking sense of sensor data in traffic analysis\nResource optimization constrained by complex business rules
DTSTART:20150818T140000
SUMMARY:Semantic Modeling for Automated Decisions
DTEND:20150818T144459
LOCATION: See Description
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