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It uses the Retrieval Augmented Generation (RAG) approach , with a structured knowledge graph in the retrieval step and is hosted on the Databricks platform which provides smooth integration of processing resources on the cloud. It offers a comprehensive suite of features designed to streamline research and discovery.
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.
The use of knowledge graphs doesn’t try to enforce yet another format on the data but instead overlays a semantic data fabric, which virtualizes the data at a level of abstraction more closely to how the users want to make use of the data. Maximize the usability of your data. Make it easy to maintain and evolve your data fabric.
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. Solution overview The AWS Serverless Data Analytics Pipeline reference architecture provides a comprehensive, serverless solution for ingesting, processing, and analyzing data.
This post looks at a specific clinical trial scoping example, powered by a knowledge graph that we have built for the EU funded project FROCKG , where both Ontotext and metaphacts are partners. Visual Ontology Modeling With metaphactory. Let’s first have a look at the knowledge graph management capabilities provided by metaphactory.
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