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On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Thanks to their might, now scientists and practitioners can develop innovative ways of collecting, storing, processing, and, ultimately, finding patterns in data.

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The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

KGs bring the Semantic Web paradigm to the enterprises, by introducing semantic metadata to drive data management and content management to new levels of efficiency and breaking silos to let them synergize with various forms of knowledge management. Schema.org and Linked Open Data are just two incarnations of the Semantic Web vision.

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Knowledge Graphs and Healthcare

Ontotext

Compared with other industries, healthcare has a fair amount of structured data, which is helpful. Ontotext’s GraphDB’s unique inference capabilities infer new knowledge from existing facts, which adds extra explanatory power to their knowledge discovery. They have to because people’s lives are at stake.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

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 structured data and context provided by knowledge graphs. We get this question regularly.

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

There are several characteristics of the knowledge graph that make this possible: The formal semantics of the RDF standards stack, and the use of ontologies , allows data and metadata of different types to be represented in a KG in such a way that they can be analyzed and interpreted together, without ambiguity, clashes or loss of information.