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Knowledge graphs (KG) came later, but quickly became a powerful driver for adoption of Semantic Web standards and all species of semantic technology implementing them. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.
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”.
This solution empowers organizations to unlock their data assets’ potential across industries like research, legal, healthcare, enterprise knowledge management, customer experience, and marketing. Organizations can handle spikes in demand seamlessly without manual capacity planning or infrastructure provisioning.
Here, I will draw upon our own experience from client projects and lessons learned to provide a selection of optimal use cases for knowledge graphs and semantic solutions along with real world examples of their applications. For many organizations, however, the question remains, “Is it the right solution for us?” million users.
Optimizing query flexibility : Building flexible queries requires a rich model. 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.
Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. As a hub for data, metadata, and content, they provide a unified, consistent, and unambiguous view of data scattered across different systems.
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