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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. Again, the overall aim is to extract knowledge from data and, through algorithms based on artificial intelligence, to assist medical professionals in routine diagnostics processes.
This means the creation of reusable data services, machine-readable semantic metadata and APIs that ensure the integration and orchestration of data across the organization and with third-party external data. Knowledge Graphs are the Warp and Weft of a Data Fabric. This provides a solid foundation for efficient data integration.
Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.
At its core, this architecture features a centralized data lake hosted on Amazon Simple Storage Service (Amazon S3), organized into raw, cleaned, and curated zones. By providing summaries, extracting insights, and enriching with metadata, you efficiency add innovative features that provide differentiated user experiences.
The examples below use OpenAI’s ChatGPT, but they can be applied against other LLM chatbots, including self-hosted ones. Let’s see how we’ve approached this with our Ontotext Knowledge Graph project. How Ontotext uses RAG We build on top of our products GraphDB and Ontotext Metadata Studio to develop a content enrichment system.
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