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Knowledge graphs (KG) came later, but quickly became a powerful driver for adoption of Semantic Web standards and all species of semantic technology implementing them. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.
These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledgediscovery. Thanks to their might, now scientists and practitioners can develop innovative ways of collecting, storing, processing, and, ultimately, finding patterns in data. Certainly not!
The Data Fabric paradigm combines design principles and methodologies for building efficient, flexible and reliable data management ecosystems. Knowledge Graphs are the Warp and Weft of a Data Fabric. To implement any Data Fabric approach, it is essential to be able to understand the context of data.
Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structureddata and context provided by knowledge graphs. Linked Data, subscriptions, purchased datasets, etc.).
Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. With the help of natural language processing (NLP), text documents can also be integrated with knowledge graphs.
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