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Problem
Knowledge Graph intelligence not accessible to business users
Business intelligence is encoded into the knowledge graph, but business users are unable to query, extract, or explore this intelligence.
Solved by these Interventions
Integrate a Large Language Model (LLM) AI with a Knowledge Graph
Connect a Large Language Model AI service with a knowledge graph so that the information source is scoped and accurate. This provides a reliable information repository for using the LLM in a specific business area.
Technologies used
Machine Learning (ML)
Large Language Model (LLM) AI
Retrieval-Augmented Generation (RAG) with LLMs
Knowledge Graphs
Integrate ChatGPT with a Knowledge Graph
Integrate the popular LLM AI "ChatGPT" with a Knowledge Graph, so that an organization can use its own scoped and structured knowledge to provide the reference information for the AI.
Technologies used
Machine Learning (ML)
Large Language Model (LLM) AI
KGaaS - Knowledge Graph as a Service
ChatGPT - the LLM AI Product by OpenAI
Knowledge Graphs
Improved by these Interventions
Deploy a RAG LLM stack with a Knowledge Graph
Use a Knowledge Graph as the source for information retrieval to provide the LLM with correct, scoped, and contextual information.
Technologies used
GraphRAG
Machine Learning (ML)
Artificial Intelligence (AI)
Large Language Model (LLM) AI
Retrieval-Augmented Generation (RAG) with LLMs
Prevented by these Capabilities
AI services that enrich unstructured media with Knowledge Graph data
A business has services in place that automatically attach structured metadata conforming to a knowledge graph model to unstructured media. This might be archive media, live intelligence streaming from the web, confidential data, and more.
Inhibits these Capabilities
Ability to view realtime actionable intelligence
Data Language regularly delivers the capability for staff, partners, and all business users to use live, real-time intelligence data.
Leads to these Problems
Subject Matter Experts cannot visually explore core business data