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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!
We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledgediscovery. Below, we outline the two directions in which we at Ontotext see and build the Semantic Web.
Compared with other industries, healthcare has a fair amount of structureddata, which is helpful. The knowledge graph seamlessly connects proprietary internal data with open public data to provide a single comprehensive view. They have to because people’s lives are at stake.
Connecting the data in a graph allows concepts and entities to complement each other’s description. Given a critical mass of domain knowledge and good level of connectivity, KG can serve as context that helps computers comprehend and manipulate data. Maximize the usability of your 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.).
The use of knowledge graphs has an enormous effect on various systems and processes which is why Garner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the enterprise.
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