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DESCRIPTION:Click for Latest Location Information: http://edw2021.dataversi
 ty.net/sessionPop.cfm?confid=133&proposalid=12866\nKnowledge graph technolo
 gy enables question answering across data silos at scale. In order to 
 provide an understanding of the data and explain the results returned by co
 mplex queries, machine learning, and other applications, a common vocabular
 y is essential. \nIndustry-standard models are often criticized due to
  their complexity, including significant breadth and depth. This is a 
 consequence of the unique position that an industry-level ontology plays wi
 th respect to other data models. In short, an industry ontology has to
  anticipate a wide range of design options in enterprise data models &ndash
 ; and include elements that reflect those options – to medi
 ate viewpoints. \nHow can an industrial ontology manage this complexit
 y and make it usable at an enterprise level? Through modeling patterns that
  allow for the range of variation required while allowing the model to be u
 nderstandable and applicable to enterprise data modelers. \nWe illustr
 ate some of these patterns and this effect with specific examples from FIBO
  (Financial Industry Business Ontology) mappings to enterprise data models.
  Lessons learned in developing and using these patterns not only in fi
 nance, but for automotive, retail, pharmaceutical, and industrial applicati
 ons, provide a level of confidence in their general applicability.\n
DTSTART:20210422T144000
SUMMARY:Modeling Patterns for Strategic Business Data Analysis Using Knowle
 dge Graphs
DTEND:20210422T152959
LOCATION: See Description
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