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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!
The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases.
Compared with other industries, healthcare has a fair amount of structureddata, which is helpful. This is where experience counts and Ontotext has a proven methodology for semantic datamodeling that normalizes both data schema and instances to concepts from major ontologies and vocabularies used by the industry sector.
There must be a representation of the low-level technical and operational metadata as well as the ‘real world’ metadata of the business model or ontologies. Connecting the data in a graph allows concepts and entities to complement each other’s description. Create a human AND machine-meaningful datamodel.
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. We get this question regularly. million users.
Knowledge Graphs Defined and Why Semantics (and Ontologies) Matter According to Wikipedia , a knowledge graph is a knowledge base that uses a graph-structureddatamodel or topology to represent and operate on data. Ontologies ensure a shared understanding of the data and its meanings.
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