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Contextualdata understanding Data systems often cause major problems in manufacturing firms. Various initiatives to create a knowledge graph of these systems have been only partially successful due to the depth of legacy knowledge, incomplete documentation and technical debt incurred over decades.
The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge. What is a knowledge graph?
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