Knowledge graphs: the missing link in enterprise AI

Knowledge graphs reduce hallucinations, he says, but they also help solve the explainability challenge. Knowledge graphs sit on top of traditional databases, providing a layer of connection and deeper understanding, says Anant Adya, EVP at Infosys. “You can do better contextual search,” he says. “And it helps you drive better insights.”

Infosys is now running proof of concepts to use knowledge graphs to combine the knowledge the company has gathered over many years with gen AI tools. “We’re identifying those use cases where they can make a bigger impact,” he says. They include automated knowledge extraction, budgeting, procurement, and enterprise planning. “But it’s very early,” he adds. “It’s still not in production.”

One company that’s deployed a knowledge graph to improve gen AI performance, and wrote about, is LinkedIn. In a paper published in April, LinkedIn reports that combining RAG with a knowledge graph helped it improve the accuracy of a customer service gen AI application by 78%. And, over the preceding six months, the combo was used by LinkedIn’s customer service team, reducing the median per-issue resolution time by 29%.

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